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Bayesian Net References

Version 4

13 July 2008

This document contains a list of references to publications and reports about Bayesian Net technology, and especially Bayesian Net applications. The report will be regularly updated and we welcome suggestions for new references to be added. Please send new references for inclusion to [email protected]

Agena Limited 32-33 Hatton Garden London EC1N 8DL UK 1. Abdel-Hamid, T. K. (1996). The slippery path to productivity improvement. IEEE Software, 13(4), 43-52 2. Abderrahim, D., L. Bernard, et al. (2006). TIDES - Using Bayesian Networks for Student Modeling. Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies, IEEE Computer Society: 1002 - 1007 3. Abramson, B. (1994). "The design of belief network-based systems for price forecasting." Computers & Electrical Engineering 20(2): 163-180 4. Abramson, B., J. Brown, et al. (1996). "HAILFINDER: A Bayesian system for predicting extreme weather." International Journal of Forecasting 7: 57-78 5. Ackerman, F. and C. Eden (2005). "Using Causal Mapping with Group Support Systems to Elicit an Understanding of Failure in Complex Projects: Some Implications for Organizational Research." Group Decision and Negotiation 14: 355–376 6. Ackermann, F., C. Eden, et al. (1997). "Modeling for Litigation: Mixing Qualitative and Quantitative Approaches." Interfaces 27: 48-65 7. Aires, F., C. Prigent, et al. (2004). "Neural network uncertainty assessment using Bayesian statistics: a remote sensing application." Neural Comput 16(11): 2415- 58 8. Aitken, C. (1996). "Lies, damned lies and expert witnesses." Mathematics Today (Bulletin of the IMA) 32(5/6): 76-80 9. Aitken, C., F. Taroni, et al. (2003). "A graphical model for the evaluation of cross- transfer evidence in DNA profiles." Theoretical Population Biology 63: 179-190

10. Aitken, C. G. G. (2004 ). Statistical interpretation of evidence: Bayesian analysis, Joseph Bell Centre for Forensic Statistics & Legal Reasoning http://www.cfslr.ed.ac.uk/publications/a001.pdf. 11. Aitken, C. G. G., T. Connolly, et al. (1995). Bayesian belief networks with an application in specific case analysis. Computational Learning and Probabilistic Reasoning. A. Gammerman, John Wiley and Sons Ltd. 12. Aitken, C. G. G., T. Connolly, et al. (1996). "Statistical modelling in specific case analysis." Science & Justice: 36(4): 245-255 13. Aktaşa, E., F. Ülengin, et al. (2007). "A decision support system to improve the efficiency of resource allocation in healthcare management." Socio-Economic Planning Sciences 41(2): 130-146 14. Aliferis, C. F. and G. F. Cooper (1996). An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief Networks Using Simulated Data Sets. Section of Medical Informatics & Intelligent Systems Program,University of Pittsburg 15. Aliferis, C. F. and G. F. Cooper (1996). A Structurally and Temporally Extended Bayesian Belief Network Model: Definitions, Properties, and Modelling Techniques. [email protected] 16. Alterovitz, G., M. Xiang, et al. (2007). "GO PaD: the Gene Ontology Partition Database." Nucleic Acids Res 35(Database issue): 322-7 17. Alvarez, S. M., B. A. Poelstra, et al. (2006). "Evaluation of a Bayesian decision network for diagnosing pyloric stenosis." J Pediatr Surg 41(1): 155-61; discussion 155-61 18. Amasaki, S., O. Mizuno, et al. (2003). A Bayesian Belief Network for Predicting Residual Faults in Software Products. Proceedings of 14th International Symposium on Software Reliability Engineering (ISSRE2003), November, pp. 215-22 19. An, X., D. Jutla, et al. (2006). Privacy intrusion detection using dynamic Bayesian networks. Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet. Fredericton, New Brunswick, Canada, ACM: 208 - 215 20. Anderson, S. K., K. G. Olesen, et al. (2000). HUGIN - a shell for building Bayesian belief universes for expert systems. 11th Intl Joint Conf Artifical Intelligence. Detroit: 1080-1085 21. Andreassen, M. Woldbye, et al. (1987). MUNIN: a causal probabilistic network for interpretation of electromyographic findings. 10th International Joint Conference on Artificial Intelligence. Milan, Italy: 366-372 22. Andreassen, S., F. Jensen, et al. (1991). "Medical expert systems based on causal probabilistic networks." Int J Biomed Comput 28(1-2): 1-30 23. Andreassen, S., C. Riekehr, et al. (1999). "Using probabilistic and decision-theoretic methods in treatment and prognosis modeling." Artif Intell Med 15(2): 121-34

24. Antal, P., G. Fannes, et al. (2003). "Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection." Artif Intell Med 29(1-2): 39-60 25. Antal, P., G. Fannes, et al. (2004). "Using literature and data to learn Bayesian networks as clinical models of ovarian tumors." Artif Intell Med 30(3): 257-81

26. Arens, D. A. (1982). "Widowhood and well-being: an examination of sex differences within a causal model." Int J Aging Hum Dev 15(1): 27-40 27. Aronsky, D., M. Fiszman, et al. (2001). "Combining decision support methodologies to diagnose pneumonia." Proc AMIA Symp: 12-6 28. Aronsky, D. and P. J. Haug (1998). "Diagnosing community-acquired pneumonia with a Bayesian network." Proc AMIA Symp: 632-6 29. Aronsky, D. and P. J. Haug (2000). "Automatic identification of patients eligible for a pneumonia guideline." Proc AMIA Symp: 12-6 30. Astakhov, V. and A. Cherkasov (2005). "Prediction of HLA-A2 binding peptides using Bayesian network." Bioinformation 1(2): 58-63 31. Athanasiou, M. and J. Y. Clark (2007). A Bayesian Network Model for the Diagnosis of the Caring Procedure for Wheelchair Users with Spinal Injury. Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07) 433-438 32. Bacon, P. J., J. D. Cain, et al. (2002). "Belief network models of land manager decisions and land use change." Journal of Environmental Management 65(1): 1- 23 33. Bahrami, H. (2006). "Causal models in primary open angle glaucoma." Ophthalmic Epidemiol 13(4): 291-8 34. Bai, C.-G. (2005). "Bayesian network based software reliability prediction with an operational profile." J. Syst. Softw. 77(2): 103-112 35. Bai, C. G., Q. P. Hu, et al. (2005). "Software failure prediction based on a Markov Bayesian network model." J. Syst. Softw. 74(3): 275-282 36. Baker, M. (2000). Diagnostic system utilizing a Bayesian network model having link weights updated experimentally. Patent number: 6076083 37. Bang, J. W. and D. Gillies (2002). Using Bayesian Networks to Model the Prognosis of Hepatitis C. In 7th Workshop on Intelligent Data Analysis in Medicine and Pharmacology, pages 7.15, Lyon, France 38. Bang, J. W. and D. Gillies (2002). Using Bayesian Networks with Hidden Nodes to Recognise Neural Cell Morphology. In M. Ishizuka and A. Satter, editors, 7th Pacific Rim International Conference on Arti_cial Intelligence, pages 385.394, Tokyo, New York,. Springer 39. Bangsø, O. and P. H. Wuillemin (2000). Top-down construction and repetitive structures representation in Bayesian networks. Proceedings of The Thirteenth International Florida Artificial Intelligence Research Symposium Conference. Florida, USA: 282-286 40. Barahona, P. (1994). "A causal and temporal reasoning model and its use in drug therapy applications." Artif Intell Med 6(1): 1-27 41. Barker, G. C. (2004). Application of Bayesian Belief Network models to food safety science 42. Batchelor, C. and J. Cain (1999). "Application of belief networks to water management studies." Agricultural Water Management 40(1): 51-57 43. Bate, A. (2007). "Bayesian confidence propagation neural network." Drug Saf 30(7): 623-5 44. Bate, A., M. Lindquist, et al. (1998). "A Bayesian neural network method for adverse drug reaction signal generation." Eur J Clin Pharmacol 54(4): 315-21 45. Bate, A., M. Lindquist, et al. (2002). "A data mining approach for signal detection and analysis." Drug Saf 25(6): 393-7 46. Bate, A., M. Lindquist, et al. (2002). "Data-mining analyses of pharmacovigilance signals in relation to relevant comparison drugs." Eur J Clin Pharmacol 58(7): 483-90 47. Bauer, E., D. Koller, et al. (1997). Update rules for parameter estimation in Bayesian networks. In Geiger D. and Shenoy P. (Eds.) Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., San Francisco, pp. 3-13 48. Bayesware. (2007). "Bayesware Knowledge Discovery by Bayesian Networks." 49. http://www.bayesware.com/. 50. Beach, B. (1975). "Expert judgment about uncertainty: Bayesian decision making in realistic settings." Organ Behav Hum Perform 14(1): 10-59 51. Bearfield, G. and W. Marsh (2005). Generalising Event Trees Using Bayesian Networks with a Case Study of Train Derailment. in Proceedings of the 24th International Conference on Computer Safety, Reliability and Security, SAFECOMP 2005, Springer-Verlag, vol. 3688 52. Beghin, I., A. De Muynck, et al. (1989). "Can the causal model approach contribute to the study of the epidemiology and the control of sleeping sickness?" Ann Soc Belg Med Trop 69 Suppl 1: 31-47; discussion 144 53. Bellamy, S. L., J. Y. Lin, et al. (2007). "An introduction to causal modeling in clinical trials." Clin Trials 4(1): 58-73 54. Ben Salem, A., A. Muller, et al. (2006). "Dynamic Bayesian Networks in system reliability analysis in 6th IFAC Symposium on Fault Detection, Supervision and Safety of technical processes." 6th IFAC Symposium on Fault Detection, Supervision and Safety of technical processes, China [hal-00092032 - version 1] (2006-09-08" 55. Bernardo, J. A. and A. F. Smith (1994). Bayesian Theory, John Wiley and Sons, New York. 56. Bibi, S. and I. Stamelos (2004). Software Process Modeling with Bayesian Belief Networks. 10th International Software Metrics Symposium (Metrics 2004). Chicago, USA 57. Biedermann, A., F. Taroni, et al. (2005). "The evaluation of evidence in the forensic investigation of fire incidents. Part II. Practical examples of the use of Bayesian networks." Forensic Science International 147(1): 59-69 58. Birckmayer, J. D., H. D. Holder, et al. (2004). "A general causal model to guide alcohol, tobacco, and illicit drug prevention: assessing the research evidence." J Drug Educ 34(2): 121-53 59. Blackburn, J. D., G. D. Scudder, et al. (1996). Improving speed and productivity of software development: a global survey of software developers. IEEE Transactions on Software Engineering, 22(12), 875-885 60. Blanco, R., I. Inza, et al. (2005). "Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS." J Biomed Inform 38(5): 376-88 61. Bobbio, A., L. Portinale, et al. (2001). "Improving the analysis of dependable systems by mapping fault trees into Bayesian networks." Reliability Engineering and System Safety 71(3): 249-260 62. Bockhorst, J., M. Craven, et al. (2003). "A Bayesian network approach to operon prediction." Bioinformatics 19(10): 1227-35 63. Boer, R., S. Plevritis, et al. (2004). "Diversity of model approaches for breast cancer screening: a review of model assumptions by the Cancer Intervention and Surveillance Network (CISNET) Breast Cancer Groups." Stat Methods Med Res 13(6): 525-38 64. Borsuk, M. E., C. A. Stow, et al. (2004). "A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis." Ecological Modelling 173(2-3): 219-39 65. Bothtner, U., S. E. Milne, et al. (2002). "Bayesian probabilistic network modeling of remifentanil and propofol interaction on wakeup time after closed-loop controlled anesthesia." J Clin Monit Comput 17(1): 31-6 66. Bottcher, P., R. Stoddard, et al. (1995). A Bayesian approach for modeling quality in software products and processes. Proc 5th Int Conf Software Quality, 214-253

67. Bouckaert, R. R. (1996). A Stratified Simulation Scheme for Inference in Bayesian Belief Networks. Utrecht University, Department of Computer Science, P.O.Box 80.089 3508 TB Utrecht, The Netherlands, email: [email protected] 68. Boudali, H. and J. B. Dugan (2005). "A discrete-time Bayesian network reliability modeling and analysis framework." Reliability Engineering and System Safety 87(3): 337-349 69. Boudali, H. and J. B. Dugan (2006). "A Continuous-Time Bayesian Network Reliability Modeling and Analysis framework." IEEE Transactions on Reliability 55: 86-97 70. Bouissou, M., F. Martin, et al. (1999). "Assessment of a Safety-Critical System Including Software: A Bayesian Belief Network for Evidence Sources." "Free. Ann. Reliability and Maintainability Symp., RAMS" 71. Boutilier, C., N. Friedman, et al. (1996). "Context-specific independence in Bayesian networks." "In Proc. 12th UAI, pages 115-123" 72. Bradford, J., C. Needham, et al. (2006). "Insights into protein-protein interfaces using a Bayesian network prediction method." J Mol Biol 362(2): 365-86 73. Brage, D. and W. Meredith (1994). "A causal model of adolescent depression." J Psychol 128(4): 455-68 74. Brewer, M. J. (2003). "Discretisation for inference on Bayesian mixture models." "Statistics and Computing 13, 209-219" 75. Brown, L. E., I. Tsamardinos, et al. (2004). "A novel algorithm for scalable and accurate Bayesian network learning." Medinfo 11(Pt 1): 711-5 76. Bryan, B. and M. Garrod (2006). Combining rapid field assessment with a Bayesian network to prioritise investment in watercourse protection, CSIRO Land and Water Science Report 10/06, April, www.clw.csiro.au/publications/science/2006/sr10-06.pd 77. Bulashevska, S., O. Szakacs, et al. (2004). "Pathways of urothelial cancer progression suggested by Bayesian network analysis of allelotyping data." Int J Cancer 110(6): 850-6 78. Burden, F. R. and D. A. Winkler (2005). "Predictive Bayesian neural network models of MHC class II peptide binding." J Mol Graph Model 23(6): 481-9 79. Burge, J., T. Lane, et al. (2007). "Discrete dynamic Bayesian network analysis of fMRI data." Hum Brain Mapp 80. Burnside, E., D. Rubin, et al. (2006). "Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience." Radiology 240(3): 666-73 81. Burnside, E., D. Rubin, et al. (2000). "A Bayesian network for mammography." Proc AMIA Symp: 106-10 82. Burnside, E., D. Rubin, et al. (2004). "Using a Bayesian network to predict the probability and type of breast cancer represented by microcalcifications on mammography." Medinfo 11(Pt 1): 13-7 83. Burnside, E. S. (2005). "Bayesian networks: computer-assisted diagnosis support in radiology." Acad Radiol 12(4): 422-30 84. Burnside, E. S., D. L. Rubin, et al. (2006). "Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience." Radiology 240(3): 666-73 85. Burnside, E. S., D. L. Rubin, et al. (2004). "Using a Bayesian network to predict the probability and type of breast cancer represented by microcalcifications on mammography." Medinfo 11(Pt 1): 13-7 86. Burnside, E. S., D. L. Rubin, et al. (2004). "A probabilistic expert system that provides automated mammographic-histologic correlation: initial experience." AJR Am J Roentgenol 182(2): 481-8 87. Buxton, H. (1997). "Advanced visual surveillance using Bayesian networks." "COLLOQUIUM DIGEST- IEE, , ISSUE 74, pages" 88. Call, C. and P. Gonsalves (2006). Belief Network-based Situation Assessment for Air Operations Centers. Proceedings of SPIE Defense & Security, Orlando, FL. 89. Campos, L. M. d., J. A. Gámez, et al. (2001). "Accelerating chromosome evaluation for partial abductive inference in Bayesian networks by means of explanation set absorption." International Journal of Approximate Reasoning, 27(2): 121-142

90. Canol, R., C. Sordo, et al. (2004). Applications of Bayesian Networks in Meteorology. Advances in Bayesian Networks. Gamez, Springer: 309-327. 91. Card, D. (1998). Learning from our mistakes with defect causal analysis. IEEE Software, 15(1), 56-63 92. Castillo, E., J. M. Gutierrez, et al. (1997). Sensitivity analysis in discrete Bayesian networks. IEEE Transactions on Systems, Man and Cybernetics, Part A, Volume: 27, Issue: 4 , July, 412 - 423 93. Chakraborty, S., M. Ghosh, et al. (2005). "Bayesian neural networks for bivariate binary data: an application to prostate cancer study." Stat Med 24(23): 3645-62

94. Chan, H. and A. Darwiche (2005). "On the Revision of Probabilistic Beliefs Using Uncertain Evidence." Artificial Intelligence 163(67-90) 95. Chang, J., K. Hwang, et al. (2005). "Bayesian network learning with feature abstraction for gene-drug dependency analysis." J Bioinform Comput Biol 3(1): 61-77 96. Chang, K. C. and Fung (1997). "Target identification with Bayesian networks in a multiple hypothesis tracking system." OPT. Eng 36(3): 684-691 97. Charniak, E. (1991). "Bayesian Networks without tears." AI Magazine, AAAI Winter: 50-6 98. Chavira, M. and A. Darwiche (2007). Compiling Bayesian Networks Using Variable Elimination. 20th International Joint Conference on Artificial Intelligence (IJCAI). Hyderabad, India 99. Chavira, M., A. Darwiche, et al. (2006). "Compiling Relational Bayesian Networks for Exact Inference." International Journal of Approximate Reasoning (IJAR) 42: 4- 20 100. Chen, R. and E. H. Herskovits (2007). "Clinical diagnosis based on bayesian classification of functional magnetic-resonance data." Neuroinformatics 5(3): 178-88 101. Chen, X., M. Chen, et al. (2006). "BNArray: an R package for constructing gene regulatory networks from microarray data by using Bayesian network." Bioinformatics 22(23): 2952-4 102. Cheng, J. (2001). Belief Network Powersoft System, University of Alberta, http://www.cs.ualberta.ca/~jcheng/bnsoft.ht 103. Cheng, J., D. A. Bell, et al. (1997). "An Algorithm for Bayesian Belief Network Construction from Data." http://www.cs.ualberta.ca/~jcheng/Doc/aistat97.pdf.

104. Cheng, P. W. and L. R. Novick (1990). "A probabilistic contrast model of causal induction." J Pers Soc Psychol 58(4): 545-67 105. Chickering, D. M. (1996). Learning Bayesian Networks is NP-Complete. LECTURE NOTES IN STATISTICS SPRINGER VERLAG. 112: 121-130. 106. Chickering, D. M. and D. Heckerman (1997). ".Efficient, Approximations for the Marginal Likelihood of Bayesian Networks with Hidden Variables." MACHINE LEARNING 29(2/3): 181-212 107. Chien, C.-F., S.-L. Chen, et al. "Using Bayesian network for fault location on distribution feeder." IEEE Trans Power Delivery 17(13): 785- 793 108. Chulani, S., B. Boehm, et al. (1998). Calibrating Software Cost Models Using Bayesian Analysis. USC-CSE 1998 109. Chulani, S., B. Boehm, et al. (1999). Bayesian analysis of empirical software engineering cost models. IEEE Transactions on Software Engineering, 25(4), 573-583 110. Chung, L., A. W. Pan, et al. (2003). "A causal model of rehabilitation resource use for subjects with spinal cord injury in Taiwan." J Rehabil Med 35(5): 208-12

111. Clarke, S. R., M. Bailey, et al. (2008). "Successful applications of statistical modeling to betting markets." Mathematics Today (Bulletin of the IMA) 44(1): 38-44 112. Cobb, B. and P. Shenoy (2005). "On the plausibility transformation method for translating belief function models to probability models." Int J Approx Reason 41(3): 314-40 113. Cofi˜no, A. S., R. Cano, et al. (2002). Bayesian networks for probabilistic weather prediction. Proceedings of the 15th European Conference on Artificial Intelligence: 695-700 114. Coolen, F. P., M. Goldstein, et al. (2007). "Using Bayesian statistics to support testing of software." Journal of Risk and Reliability 221(1): 85-93 115. Cooper, G. F. and E. Herskovits (1992). A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning, 9, Page 309 116. Cooper, N., A. Sutton, et al. (2002). "Decision analytical economic modelling within a Bayesian framework: application to prophylactic antibiotics use for caesarean section." Stat Methods Med Res 11(6): 491-512 117. Cooper., G. F. (1990). "The computational complexity of probabilistic inference using bayesian belief networks ." Artificial Intelligence 42(2-3): 393-405 118. Coulter, D. M., A. Bate, et al. (2001). "Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: data mining study." BMJ 322(7296): 1207-9 119. Coupe, V. M., N. Peek, et al. (1999). "Using sensitivity analysis for efficient quantification of a belief network." Artif Intell Med 17(3): 223-47 120. Courtois, P. J., N. E. Fenton, et al. (1998). Examination of bayesian belief network for safety assessment of nuclear computer-based systems, City University, Centre for Software Reliability 121. Cowell, R., S. Lauritzen, et al. (2006). "Identification and separation of DNA mixtures using peak area information." Forensic Science International 166(1): 28-34 122. Cowell, R. G. (2003). "Finex: a Probabilistic Expert System for forensic identification." Forensic Science International 134(2): 196-206 123. Cowell, R. G., A. P. Dawid, et al. (1991). "A Bayesian expert system for the analysis of an adverse drug reaction." Artificial Intelligence in Medicine 3: 257- 270 124. Cowell, R. G., A. P. Dawid, et al. (1999). Probabilistic Networks and Expert Systems. New York, Springer 125. Cowell, R. G., A. P. Dawid, et al. (1993). "Sequential Model Criticism in Probabilistic Expert Systems." IEEE Transactions on Pattern Analysis and Machine Intelligence 15(3): 209-219 126. Cox, Z. and J. Pfautz (2007). Causal Influence Models: A Method for Simplifying Construction of Bayesian Networks (Rep. No. R-BN07-01). Cambridge, MA: Charles River Analytics Inc 127. Croft, J. and J. Q. Smith (2003). "Discrete mixtures in simple Bayesian Networks with hidden variables." J of Computational Statistics and Data Analysis 41(3-4): 539-547 128. Cruz-Ramirez, N., H. G. Acosta-Mesa, et al. (2007). "Diagnosis of breast cancer using Bayesian networks: A case study." Comput Biol Med 37(11): 1553-64 129. D’Ambrosio, B. (1999). "Inference in Bayesian Networks." AI Magazine, AAAI 20(2): 21-36 130. Dagum, P. and R. M. Chavez (1993). Approximating Probabilistic Inference in Bayesian Belief Networks. Pattern Analysis and Machine Intelligence 15(3):246- 255 131. Dagum, P. and A. Galper (1995). "Time series prediction using belief network models." International Journal of Human-Computer Studies 42(6): 617-632 132. Dahll, G. (2000). "Combining disparate sources of information in the safety assessment of software-based systems." Nuclear Engineering and Design 195(3): 307-319 133. Dahll, G. and B. A. Gran (2000). The use of Bayesian belief nets in safety assessment of software based systems. OECD Halden Reactor Project, PO Box 173, N-1751 Halden, Norway 134. Dai, C. and J. Liu (2005). "Inducing Pairwise Gene Interactions from Time- Series Data by EDA Based Bayesian Network." Conf Proc IEEE Eng Med Biol Soc 7: 7746-9 135. Darwiche, A. Constant-Space Reasoning in Dynamic Bayesian Networks http://citeseer.ist.psu.edu/489080.html. 136. Darwiche, A. (2003). "Differential Approach to Inference in Bayesian Networks." Journal of the ACM 50(3): 280-305 137. Das, B. (2004). "Generating Conditional Probabilities for Bayesian Networks: Easing the Knowledge Acquisition Problem. http://www.arxiv.org [On-line]. Available: http://www.citebase.org/cgi-bin/citations? id=oai:arXiv.org:cs/0411034." 138. Dawid, A. P. (2003). An object-oriented Bayesian network for estimating mutation rates. Ninth International Workshop on Artificial Intelligence and Statistics, ISBN 0-9727358-0-1. C. M. Bishop and B. J. Frey. Key West, Florida http://tinyurl.com/39bmh. 139. Dawid, A. P., J. Mortera, et al. (2006). Representing and solving complex DNA identification cases using Bayesian networks. Progress in Forensic Genetics 11 (Proceedings of the 21st International ISFG Congress). A. Amorim, F. Corte- Real and N. Morling. Ponta Delgada, The Azores, Portugal International Congress Series, Elsevier Science, Amsterdam. 1288: 484-91 140. Dawid, A. P., J. Mortera, et al. (2007). "Object-oriented Bayesian networks for complex forensic DNA profiling problems." Forensic Science International 169: 195-205 141. de Campos, L. M. (2006). "A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests." J. Mach. Learn. Res. 7: 2149-2187 142. de Campos, L. M. (2006). "A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests." J. Mach. Learn. Res. 7: 2149-2187 143. de Melo, A. C. V. and A. J. Sanchez (2008). "Software maintenance project delays prediction using Bayesian Networks." Expert Syst. Appl. 34(2): 908-919 http://dx.doi.org/10.1016/j.eswa.2006.10.040. 144. De Santa Olalla, F. J. M., Dominguez A, et al. (2005). "Integrated Water Resources Management of the Hydrogeological Unit "Eastern Mancha" Using Bayesian Belief Networks." Agricultural Water Management 77(1-3): 21-36 145. de Santana, A. L., C. R. Frances, et al. (2007). "Strategies for improving the modeling and interpretability of Bayesian networks." Data Knowl. Eng. 63(1): 91-107 146. Deforche, K., R. Camacho, et al. (2007). "Bayesian network analysis of resistance pathways against HIV-1 protease inhibitors." Infect Genet Evol 7(3): 382-90 147. Delic, K. A., F. Mazzanti, et al. (1997). Formalising a software safety case via belief networks. Proc DCCA-6, 6th IFIP International Working Conf on Dependable Computing for critical Appliations, Garmisch-Partenkirchen, Germany, March 148. Dembo, R., D. Farrow, et al. (1979). "Testing a causal model of environmental influences on the early drug involvement of inner city junior high school youths." Am J Drug Alcohol Abuse 6(3): 313-36 149. Deng, X., H. Geng, et al. (2006). "Joint learning of gene functions--a Bayesian network model approach." J Bioinform Comput Biol 4(2): 217-39 150. Dexheimer, J. W., L. E. Brown, et al. (2007). "Comparing decision support methodologies for identifying asthma exacerbations." Stud Health Technol Inform 129: 880-4 151. Dey, S. and J. A. Stori (2005). "A Bayesian network approach to root cause diagnosis of process variations." International Journal of Machine Tools and Manufacture 45(1): 75-91 152. Diamond, L., V. Mishka, et al. (1995). "Are normative expert systems appropriate for diagnostic pathology?" J Am Med Inform Assoc 2(2): 85-93 153. Díez, F. J. (1993). Parameter adjustment in Bayes networks: the gener-alized noisy or-gate. Ninth Conference on Uncertainty in Artificial Intelligence. D. Heckerman and A. Mamdani. Washington D.C: 99-105 154. Diez, F. J., J. Mira, et al. (1997). "DIAVAL, a Bayesian expert system for echocardiography." Artif Intell Med 10(1): 59-73 155. Dockstader, S. L. and A. M. Tekalp (2001). "Multiple camera tracking of interacting and occluded human motion." Proceedings of the IEEE 89(10): 1441- 1455 156. Donaghy, R. and A. H. Marshall (2005). Modelling the Health of ARMD Patients Eyes Using a Dynamic Bayesian Network. Conference on Applied Statistics. Ireland, Enniskillen: 98-99 157. Downe-Wamboldt, B. L. and P. M. Melanson (1998). "A causal model of coping and well-being in elderly people with arthritis." J Adv Nurs 27(6): 1109-16 158. Dray, P., G. J. Bearfield, et al. (2007). Constructing Scalable and Parameterised System Wide Risk Models. 25th International System Safety Conference. Baltimore, USA, System Safety Society 159. Druzdzel, M. J. and H. van Leijen (2001). "Causal reversibility in Bayesian networks." Journal of Experimental and Theoretical Artificial Intelligence 13(1): 45-62 160. Druzdzel, M. K. and L. C. van der Gaag (2000). "Building Probabilistic Networks: Where Do the Numbers Come From?" IEEE Transactions on Knowledge and Data Engineering 12(4): 481-486 161. Druzdzel, M. K. and L. C. vanderGaag (1995). Elicitation of probabilities for belief networks: combining qualitative and quantitiative information. Proc 11th Ann Conf on Uncertainty in Artifical Intelligence (UAI-95), 141-148, Montreal, Quebec, Canada, August 162. Eaton, D. and K. Murphy (2007). "Exact Bayesian structure learning from uncertain interventions." AI & Statistics 163. Edwards, D. (2000). Introduction to Graphical Modelling, Springer-Verlag. 164. Edwards, W. (1991). "Influence Diagrams, Bayesian Imperialism, and the Collins case: an appeal to reason." Cardozo Law Review 13: 1025-1079 165. Eleye-Datubo, A., A. Wall, et al. (2006). "Enabling a powerful marine and offshore decision-support solution through Bayesian network technique." Risk Anal 26(3): 695-721 166. Elish, M. O., D. C. Rine, et al. (2002). "Evaluating collaborative software in supporting organizational learning with Bayesian Networks." SAC '02: Proceedings of the 2002 ACM symposium on Applied computing: 992-996 http://doi.acm.org/10.1145/508791.508984. 167. Elloy, D. F., W. Terpening, et al. (2001). "A causal model of burnout among self-managed work team members." J Psychol 135(3): 321-34 168. Embrey, D. E. (1992). Incorporating management and organisational factors into probabilistic safety assessment. Reliability Engineering and System Safety, 38, 199-208 169. Erb, R. J. (1995). "The backpropagation neural network--a Bayesian classifier. Introduction and applicability to pharmacokinetics." Clin Pharmacokinet 29(2): 69-79 170. Evett, I. W., P. D. Gill, et al. (2002). "Interpreting small quantities of DNA: the hierarchy of propositions and the use of Bayesian networks." Journal of Forensic Sciences 47(3): 520-530 171. Ezawa, K., M. Singh, et al. (1996). Learning Goal-Oriented Bayesian Networks for Telecommunications Risk Management. 13th International Conference on Machine Learning. Bari, Italy: 139-147 172. Faigman, D. L. and A. J. Baglioni (1988). "Bayes' theorem in the trial process." Law and Human Behavior 12(1): 1-17 http://dx.doi.org/10.1007/BF01064271. 173. Falzon, L. (2005). "Using Bayesian network analysis to support centre of gravity analysis in military planning." European Journal of Operational Research 170 (2): 629-643 174. Fan, C.-F. and Y.-C. Yu (2004). "BBN-based software project risk management." J Systems Software 73 (2): 193-203 175. Federal Drugs Agency (2006 ). Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials - Draft Guidance for Industry and FDA Staff, U.S. Department of Health and Human Services, Food and Drug Administration http://www.fda.gov/cdrh/osb/guidance/1601.html 176. Feelders, A. J. and L. C. v. d. Gaag (2006). "Learning Bayesian network parameters under order constraints." International Journal of Approximate Reasoning (IJAR) 42: 37-53 177. Feelders, A. J. and J. Ivanovs (2006). Discriminative Scoring of Bayesian Network Classifiers: a Comparative Study. Third European workshop on probabilistic graphical models (PGM'06) M. Studen'y and J. Vomlel: 75-82 178. Fenton, N. E. (2003). SCULLY: Scaling up Bayesian Nets for Software Risk Assessment. EPSRC Project GR/N00258 Final Report. Queen Mary University of London, EPSRC http://www.dcs.qmul.ac.uk/research/radar/Projects/scully/SCULLY%20Project %20Final%20Report.pdf. 179. Fenton, N. E., B. Littlewood, et al. (1998). "Assessing dependability of safety critical systems using diverse evidence." IEE Proceedings Software 145(1): 35- 39 180. Fenton, N. E., D. W. R. Marsh, et al. (1999). SERENE (SafEty and Risk Evaluation using bayesian Nets): Method Manual https://www.dcs.qmul.ac.uk/~norman/papers/serene.pdf. 181. Fenton, N. E., W. Marsh, et al. (2004). Making Resource Decisions for Software Projects. 26th International Conference on Software Engineering (ICSE2004) Edinburgh, United Kingdom, IEEE Computer Society: 397-406 182. Fenton, N. E. and M. Neil (1999). "A critique of software defect prediction models." Software Engineering, IEEE Transactions on 25(5): 675-689 183. Fenton, N. E. and M. Neil (1999). "Software metrics: successes, failures and new directions." Journal of Systems and Software 47(2-3): 149-157 184. Fenton, N. E. and M. Neil (2000). "Bayesian belief nets: a causal model for predicting defect rates and resource requirements." Software Testing and Quality Engineering 2(1): 48-53 185. Fenton, N. E. and M. Neil (2000). "The Jury Fallacy and the use of Bayesian nets to simplify probabilistic legal arguments." Mathematics Today (Bulletin of the IMA) 36(6): 180-187 186. Fenton, N. E. and M. Neil (2001). "Making Decisions: Using Bayesian Nets and MCDA." Knowledge-Based Systems 14: 307-325 187. Fenton, N. E. and M. Neil (2007). Managing Risk in the Modern World: Bayesian Networks and the Applications, London Mathematical Society, Knowledge Transfer Report. 1

http://www.lms.ac.uk/activities/comp_sci_com/KTR/apps_bayesian_networks.p df. 188. Fenton, N. E., M. Neil, et al. (2007). "Using Ranked nodes to model qualitative judgements in Bayesian Networks." IEEE Transactions on Knowledge and Data Engineering 19(10): 1420-1432 189. Fenton, N. E., M. Neil, et al. (2002). "Software Measurement: Uncertainty and Causal Modelling." IEEE Software 10(4): 116-122 190. Fenton, N. E., M. Neil, et al. (2008). "Using Bayesian Networks to Predict Software Defects and Reliability." Proceedings of the Institution of Mechanical Engineers, Part O, Journal of Risk and Reliability to appear 191. Fenton, N. E., M. Neil, et al. (2007). "Predicting software defects in varying development lifecycles using Bayesian nets." Information & Software Technology 49: 32-43 192. Fenton, N. E., L. Radlinski, et al. (2006). Improved Bayesian Networks for Software Project Risk Assessment Using Dynamic Discretisation. Software Engineering Techniques: Design for Quality (Prceedings of Software Engineering Techniques 2006, Warsaw, Poland, 17-20 Oct 2006). K. Sacha, Springer, Boston. 227: 139-148. 193. Ferat, S., Y. M Cetin, et al. (2007). "Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization." Parallel Comput. 33(2): 124-143 194. Fernández, A. and A. Salmerón (2008). "BayesChess: A computer chess program based on Bayesian networks." Pattern Recognition Letters 29 (8): 1154- 1159 195. Fine, S. and A. Ziv (2003). Coverage Directed Test Generation for Functional Verification using Bayesian Networks. 40th Design Automation Conference: 286-291 196. Fine, S. and A. Ziv (2004). On the application of Bayesian Networks for simulation-based verification. 2nd Bayesian Modelling Applications Workshops of Uncertainty in AI, Banff, Canada 197. Fischer, E. A., J. Y. Lo, et al. (2004). "Bayesian networks of BI-RADStrade mark descriptors for breast lesion classification." Conf Proc IEEE Eng Med Biol Soc 4: 3031-4 198. Foreman, L. A., C. Champod, et al. (2003). "Interpreting DNA Evidence: A Review." Internat. Statist. Rev. 71(3): 473-495 199. Forshed, J., F. O. Andersson, et al. (2002). "NMR and Bayesian regularized neural network regression for impurity determination of 4-aminophenol." J Pharm Biomed Anal 29(3): 495-505 200. Freedman, D. A. (2004). "Graphical models for causation, and the identification problem." Eval Rev 28(4): 267-93 201. Friedman, N. and M. Goldszmidt (1998). Learning Bayesian Network from Data. SRI International. http://www.erg.sri.com/people/moises/tutorial/index.htm 202. Fu, L. D. and I. Tsamardinos (2005). "A comparison of Bayesian network learning algorithms from continuous data." AMIA Annu Symp Proc: 960 203. Fugelsang, J. A. and V. A. Thompson (2003). "A dual-process model of belief and evidence interactions in causal reasoning." Mem Cognit 31(5): 800-15 204. Fung, R. and B. Del Favero (1995). "Applying Bayesian Networks to Information Retrieval." Communications of the ACM 38(3): 42-48 205. Galliers, J., A. Sutcliffe, et al. (1999). A causal model of human error for safety- critical user interface design. City University, CHCID 206. Garbolino, P. and F. Taroni (2002). "Evaluation of scientific evidence using Bayesian networks." Forensic Sci. Int. 125 149-155 207. Garc, P., A. Amandi, et al. (2007). "Evaluating Bayesian networks' precision for detecting students' learning styles." Comput. Educ. 49(3): 794-808 208. Geiger, D. and D. Heckerman (1996). "Knowledge representation and inference in similarity networks and Bayesian multinets." Artifical Intelligence J 82((1-2)): 45-74 209. Gevaert, O., F. De Smet, et al. (2006). "Predicting the outcome of pregnancies of unknown location: Bayesian networks with expert prior information compared to logistic regression." Human Reproduction 21(7): 1824-1831 210. Gevaert, O., F. De Smet, et al. (2006). "Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks." Bioinformatics 22(14): e184-90 211. Ghabayen, S. M. S., M. McKee, et al. (2006). "Ionic and isotopic ratios for identification of salinity sources and missing data in the Gaza aquifer." Journal of Hydrology 318(1-4): 360-373 212. Gilthorpe, M., I. Maddick, et al. (2000). "Introduction to Bayesian modelling in dental research." Community Dent Health 17(4): 218-21 213. Gjerdingen, D. K., D. G. Froberg, et al. (1990). "A causal model describing the relationship of women's postpartum health to social support, length of leave, and complications of childbirth." Women Health 16(2): 71-87 214. Glymour, C. (2001). The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology. Cambridge, MA: , The MIT Press. 215. Golightly, A. and D. J. Wilkinson (2006). "Bayesian sequential inference for stochastic kinetic biochemical network models." J Comput Biol 13(3): 838-51

216. Gong, S., J. Ng, et al. (2002). "On the semantics of visual behaviour, structured events and trajectories of human action." Image and Vision Computing 20(12): 873-888 217. Goubanova, O. and S. King (2008). "Bayesian networks for phone duration prediction." Speech Commun. 50(4): 301-311 218. Gran, B. A. (2002). "Assessment of programmable systems using Bayesian belief nets 219. " Safety Science 40(9): 797-812 220. Gras, J.-J. (2004). "End-to-End Defect Modeling." IEEE Software 21(5): 98-100 http://doi.ieeecomputersociety.org/10.1109/MS.2004.1331312. 221. Green, N. (2005). "A Bayesian network coding scheme for annotating biomedical information presented to genetic counseling clients." J Biomed Inform 38(2): 130-44 222. Greenland, S. (1990). "Randomization, statistics, and causal inference." Epidemiology 1(6): 421-9 223. Greenland, S. and B. Brumback (2002). "An overview of relations among causal modelling methods." Int J Epidemiol 31(5): 1030-7 224. Greenland, S. and H. Morgenstern (2001). "Confounding in health research." Annu Rev Public Health 22: 189-212 225. Greenland, S., J. Pearl, et al. (1999). "Causal diagrams for epidemiologic research." Epidemiology 10(1): 37-48 226. Gregoriades, A. and A. Sutcliffe (2005). "Scenario-based assessment of nonfunctional requirements." Software Engineering, IEEE Transactions on 31(5): 392-409 227. Griffin, H. C., C. L. Fitch, et al. (2004). "The causal pathway model and cerebral palsy." Neonatal Netw 23(6): 43-7 228. Haddawy, P., J. Jacobson, et al. (1997). "BANTER: a Bayesian network tutoring shell." Artif Intell Med 10(2): 177-200 229. Haddawy, P., C. E. Kahn, Jr., et al. (1994). "A Bayesian network model for radiological diagnosis and procedure selection: work-up of suspected gallbladder disease." Med Phys 21(7): 1185-92 230. Hajmeer, M. N. and I. A. Basheer (2003). "A hybrid Bayesian-neural network approach for probabilistic modeling of bacterial growth/no-growth interface." Int J Food Microbiol 82(3): 233-43 231. Hall, J. A., M. A. Milburn, et al. (1993). "A causal model of health status and satisfaction with medical care." Med Care 31(1): 84-94 232. Halliwell, J., J. Keppens, et al. (2003). "Linguistic Bayesian Networks for reasoning with subjective probabilities in forensic statistics." ICAIL '03: Proceedings of the 9th international conference on Artificial intelligence and law: 42-50 233. Hamilton, P. W., N. Anderson, et al. (1994). "Expert system support using Bayesian belief networks in the diagnosis of fine needle aspiration biopsy specimens of the breast." J Clin Pathol 47(4): 329-36 234. Hansen, C. P. (1989). "A causal model of the relationship among accidents, biodata, personality, and cognitive factors." J Appl Psychol 74(1): 81-90 235. Hatsch, D., C. Keyser, et al. (2007). "Resolving paternity relationships using X- chromosome STRs and Bayesian networks." J Forensic Sci 52(4): 895-7 236. Hearty, P., N. Fenton, et al. (2007). "Predicting Project Velocity in XP using a Learning Dynamic Bayesian Network Model." IEEE Trans Software Eng (submitted) 237. Hearty, P., N. Fenton, et al. (2005). Automated population of causal models for improved software risk assessment. ASE '05: Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering, Long Beach, CA, USA, ACM Press. 238. Heckerman and EricHorvitz (1998). Inferring Informational Goals from Free- Text Queries: A Bayesian Approach. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, pages 230- 237. Morgan Kaufmann: San Francisco, July 239. Heckerman, D. and J. S. Breese (1995). Causal independence for probability assessment and inference using Bayesian networks. Microsoft Technical Report MSR-TR-94-08, to appear in IEEE Systems Man and Cybernetics 240. Heckerman, D., D. Geiger, et al. (1995). Learning Bayesian networks: trhe combination of knowledge and statistical data. Machine Learning, 20, 197-243 241. Heckerman, D., A. Mamdani, et al. (1995). Real-world applications of Bayesian networks. Comm ACM, 38(3), 25-26 242. Hejlesen, O., S. Andreassen, et al. (1997). "DIAS--the diabetes advisory system: an outline of the system and the evaluation results obtained so far." Comput Methods Programs Biomed 54(1-2): 49-58 243. Hejlesen, O. K., K. G. Olesen, et al. (2005). "Decision support for diagnosis of lyme disease." Stud Health Technol Inform 116: 205-10 244. Helman, P., R. Veroff, et al. (2004). "A Bayesian network classification methodology for gene expression data." J Comput Biol 11(4): 581-615 245. Helsper, E. M., L. C. v. d. Gaag, et al. (2005). Bringing order into Bayesian- network construction. Third International Conference on Knowledge Capture, New York: ACM Press: 121-128 246. Henderson, J. S. and R. W. Burn (2004). "Uptake pathways: the potential of Bayesian belief networks to assist the management, monitoring and evaluation of development-orientated research." Agricultural Systems 79(1): 3-15 247. Henriksen, H. J., P. Rasmussen, et al. (2007). "Public participation modelling using Bayesian networks in management of groundwater contamination." Environmental Modelling & Software 22(8): 1101-1113 248. Henrion, M. (1989). Some Practical Issues in Constructing Belief Networks. Uncertainty in Artificial Intelligence 3. L. Kanal, T. Levitt and J. Lemmer, North Holland: Elsevier Science: 161-173 249. Hepler, A. B., A. P. Dawid, et al. (2007). "Object-oriented graphical representations of complex patterns of evidence." Law, Probability & Risk 6(1- 4): 275-293 250. Hepler, A. B. and B. S. Weir (2004). Using Bayesian Networks for Paternity Calculations: Adding an Evolutionary Perspective. JSM (Joint Statistical Meetings) 2004. Toronto, Canada 251. Hernan, M. A. (2004). "A definition of causal effect for epidemiological research." J Epidemiol Community Health 58(4): 265-71 252. Hernan, M. A. and J. M. Robins (2006). "Instruments for causal inference: an epidemiologist's dream?" Epidemiology 17(4): 360-72 253. Herskovits, E. H. and G. F. Cooper (1991). "Algorithms for Bayesian belief- network precomputation." Methods Inf Med 30(2): 81-9 254. Hibou, M. and J.-M. Labat (2006). How to orientate arcs in a Bayesian network based student model. 6th IEEE International Conference on Advance Learning Technologies: 560-562 255. Hoeffer, B. (1987). "A causal model of loneliness among older single women." Arch Psychiatr Nurs 1(5): 366-73 256. Hofler, M. (2005). "The Bradford Hill considerations on causality: a counterfactual perspective." Emerg Themes Epidemiol 2: 11 257. Hofler, M. (2005). "Causal inference based on counterfactuals." BMC Med Res Methodol 5: 28 258. Hofler, M. (2006). "Getting causal considerations back on the right track." Emerg Themes Epidemiol 3: 8 259. Hogan, J. W. and D. O. Scharfstein (2006). "Estimating causal effects from multiple cycle data in studies of in vitro fertilization." Stat Methods Med Res 15(2): 195-209 260. Holst, A. and A. Lansner (1993). "A flexible and fault tolerant query-reply system based on a Bayesian neural network." Int J Neural Syst 4(3): 257-67 261. Hoot, N. and D. Aronsky (2005). "Using Bayesian networks to predict survival of liver transplant patients." AMIA Annu Symp Proc: 345-9 262. Horn, J., T. Birkhölzer, et al. (2001). Knowledge Acquisition and Automated Generation of Bayesian Networks for a Medical Dialogue and Advisory System. Lecture Notes In Computer Science; Vol. 2101 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine Pages: 199 - 202. 263. Horvitz, E. (1998). Lumiere Project: Bayesian Reasoning for Automated Assistance, Decision Theory & Adaptive Systems Group, Microsoft Research. Microsoft Corp. Redmond, WA http://research.microsoft.com/research/dtg/horvitz/lum.htm. 264. Horvitz, E. and M. Barry (1995). Display of information for time-critical decision making. 11th Conference on Uncertainty in Artificial Intelligence. Montreal 265. Horvitz, E., J. Breese, et al. (1998). The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence 266. Huaien, L. and S. Puthusserypady (2004). "Bayesian radial basis function network for modeling fMRI data." Conf Proc IEEE Eng Med Biol Soc 1: 450-3

267. Huang, C. and A. Darwiche (1996). "Inference in Belief Networks: A Procedural Guide." International Journal of Approximate Reasoning (IJAR) 15(3): 225-263

268. Hudson, L. D., B. S. Ware, et al. An Application of Bayesian Networks to Antiterrorism Risk Management for Military Planners www.dsbox.com/Images/UAI_Antiterrorism_Paper.pdf. 269. Hummel, R. and L. Manevitz (1996). "A statistical approach to the representation of uncertainty in beliefs using spread of opinions." 270. Husmeier, D., W. D. Penny, et al. (1999). "An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers." Neural Netw 12(4-5): 677-705 271. Huygen, P. E. M. (2002). Use of Bayesian Belief Networks in legal reasoning. 17th BILETA Annual Conference. Free University, Amsterdam http://www.bileta.ac.uk/02papers/huygen.html. 272. Hwang, K. and B. Zhang (2005). "Bayesian model averaging of Bayesian network classifiers over multiple node-orders: application to sparse datasets." IEEE Trans Syst Man Cybern B Cybern 35(6): 1302-10 273. Imoto, S., S. Kim, et al. (2003). "Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network." J Bioinform Comput Biol 1(2): 231-52 274. Imoto, S., K. Sunyong, et al. (2002). "Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network." Proc IEEE Comput Soc Bioinform Conf 1: 219-27 275. Jensen, F. V. (1996). An Introduction to Bayesian Networks. 276. Jensen, F. V., S. L. Lauritzen, et al. (1990). An algebra of bayesian belief universes for knowledge based systems. Networks, 20, 637-659 277. Jensen, F. V. and T. Nielsen (2007). Bayesian Networks and Decision Graphs, Springer-Verlag New York Inc. 278. Ji, Q., P. Lan, et al. (2006). "A Probabilistic Framework for Modeling and Real- Time Monitoring Human Fatigue." IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS 36(5): 862-875 279. Joseph, A., N. Fenton, et al. (2006). "Predicting football results using Bayesian Nets and other Machine Learning Techniques." Knowledge Based Systems 17(7): 544-553 280. Jowett, C. (2001). Sittin' in the Dock with the Bayes. New Law Journal Expert Witness Supplement of Feb 16 281. Kahn, C. E., Jr., J. J. Laur, et al. (2001). "A Bayesian network for diagnosis of primary bone tumors." J Digit Imaging 14(2 Suppl 1): 56-7 282. Kahn, C. E., Jr., L. M. Roberts, et al. (1997). "Construction of a Bayesian network for mammographic diagnosis of breast cancer." Comput Biol Med 27(1): 19-29 283. Kahn, C. J., J. Laur, et al. (2001). "A Bayesian network for diagnosis of primary bone tumors." J Digit Imaging 14(2 Suppl 1): 56-7 284. Kahn, C. J., L. Roberts, et al. (1997). "Construction of a Bayesian network for mammographic diagnosis of breast cancer." Comput Biol Med 27(1): 19-29 285. Kahn, C. J., L. Roberts, et al. (1995). "Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer." Proc Annu Symp Comput Appl Med Care: 208-12 286. Kang, C. W. and M. W. Golay (1999). "A Bayesian belief network-based advisory system for operational availability focused diagnosis of complex nuclear power systems." Expert Systems with Applications 17(1): 21-32 287. Kannan, P. R. (2007). "Bayesian networks: Application in safety instrumentation and risk reduction." ISA Transactions 46(2): 255-259 288. Karraz, G. and G. Magenes (2006). "Automatic Classification of Heartbeats using Neural Network Classifier based on a Bayesian Framework." Conf Proc IEEE Eng Med Biol Soc 1: 4016-9 289. Kayaalp, M., G. F. Cooper, et al. (2000). "Predicting ICU mortality: a comparison of stationary and nonstationary temporal models." Proc AMIA Symp: 418-22 290. Kazi, J. I., P. N. Furness, et al. (1998). "Diagnosis of early acute renal allograft rejection by evaluation of multiple histological features using a Bayesian belief network." J Clin Pathol 51(2): 108-13 291. Khodakarami, V., N. Fenton, et al. (2007). "Project Scheduling: Improved approach to incorporate uncertainty using Bayesian Networks." Project Management Journal 38: 39-49 292. Kiebel, S., M. Garrido, et al. (2007). "Dynamic causal modelling of evoked responses: the role of intrinsic connections." Neuroimage 36(2): 332-45 293. Kikuchi, T., H. Pezeshk, et al. (2007). "A Bayesian cost-benefit approach to the determination of sample size in clinical trials." Stat Med 294. Kim, J. S., J. Kaye, et al. (2001). "Moderating and mediating effects in causal models." Issues Ment Health Nurs 22(1): 63-75 295. Kim, S., S. Imoto, et al. (2004). "Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data." Biosystems 75(1-3): 57-65 296. Kline, J. A., A. J. Novobilski, et al. (2005). "Derivation and validation of a Bayesian network to predict pretest probability of venous thromboembolism." Ann Emerg Med 45(3): 282-90 297. Koller, D., U. Lerner, et al. (1999). A General Algorithm for Approximate Inference and its Application to Hybrid Bayes Nets. In Proceedings of the 15th Annual Conference on Uncertainty in AI (UAI), Stockholm, Sweden, August 1999, pages 324-333 298. Koller, D. and A. Pfeffer (1997). Object-Oriented Bayesian Networks. Proceedings of the 13th Annual Conference on Uncertainty in AI (UAI) Providence, Rhode Island: 302-313 299. Korb, K. B. and A. E. Nicholson (2004). Bayesian Artificial Intelligence, CRC Press. 300. Kozlov, A. V. and D. Koller (1997). Nonuniform dynamic discretization in hybrid networks. Proceedings of the 13th Annual Conference on Uncertainty in AI (UAI). Providence, Rhode Island: 314-325 301. Kozlov, A. V. and J. P. Singh (1996). "Parallel implementation of probabilistic inference." 302. Krause, P. (1998). "Learning probabilistic networks." The Knowledge Engineering Review 15(3): 321-351 303. Lacave, C. and F. J. Diez (2002). "A review of explanation methods for Bayesian networks." Knowl. Eng. Rev. 17(2): 107-127 304. Lam, S. K. and A. Owen (2007). "Combined resynchronisation and implantable defibrillator therapy in left ventricular dysfunction: Bayesian network meta- analysis of randomised controlled trials." BMJ 305. Lam, W. and F. Bacchus (1994). Learning Bayesian Belief Networks: An Approach based on the MDL Principle. Computation Intelligence Vol 10:4, pages 269--293 306. Langseth, H. (2002). Bayesian Networks with Applications in Reliability Analysis. Dept. of Mathematical Sciences, Norwegian University of Science and Technology. PhD 307. Langseth, H. and L. Portinale (2007). "Bayesian networks in reliability." Reliab Eng Syst Saf 92(1): 92-108 308. Lansner, A. and A. Holst (1996). "A higher order Bayesian neural network with spiking units." Int J Neural Syst 7(2): 115-28 309. Laskey, K. and S. Mahoney (1998). Network fragments: representing knowledge for constructing probabilistic model networks. 13th Annual Conference on Uncertainty in AI http://site.gmu.edu/~klaskey/lectures.html. 310. Laskey, K. B. (1995). Sensitivity Analysis for Probability Assessments in Bayesian Networks. IEEE Transactions on Systems, Man, and Cybernetics 25(6) pp. 901-909 311. Laskey, K. B. and S. M. Mahoney (2005). "Network engineering for agile belief network models." IEEE Transactions on Knowledge and Data Engineering 12 (4): 487-498 312. Laskey, K. B. and S. Mahoney. (2000). "Network Engineering for Agile Belief Network Models." IEEE Transactions on Knowledge and Data Engineering 12(4): 487-498 313. Lauría, E. J. M. and P. J. Duchessi (2006). "A Bayesian Belief Network for IT implementation decision support." Decision Support Systems 42(3): 1573-1588

314. Lauritzen, S. L. (1996). Graphical Models, Clarendon Press, Oxford. 315. Lauritzen, S. L. and D. J. Spiegelhalter (1988). Local Computations with Probabilities on Graphical Structures and their Application to Expert Systems (with discussion). J. R. Statis. Soc. B, 50, No 2, pp 157-224 316. Le Duff, F., C. Muntean, et al. (2004). "Predicting survival causes after out of hospital cardiac arrest using data mining method." Medinfo 11(Pt 2): 1256-9 317. Lederer, A. L. and J. Prasad (1998). "A causal model for software cost estimating error." 318. Lee, C.-J. and K. J. Lee (2005). "Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal." Reliability Engineering & System Safety 91(5): 515-532 319. Lee, P. M. (1997). Bayesian Statistics: An Introduction, 2nd Edn. 320. Lee, S.-M. and P. A. Abbott (2003). "Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers." J. of Biomedical Informatics 36(4/5): 389-399 321. Leegon, J. and D. Aronsky (2006). "Impact of different training strategies on the accuracy of a Bayesian network for predicting hospital admission." AMIA Annu Symp Proc: 474-8 322. Leegon, J., I. Jones, et al. (2005). "Predicting hospital admission for Emergency Department patients using a Bayesian network." AMIA Annu Symp Proc: 1022

323. Lehmann, H. and E. Shortliffe (1991). "Thomas: building Bayesian statistical expert systems to aid in clinical decision making." Comput Methods Programs Biomed 35(4): 251-60 324. Leibovici, L., M. Fishman, et al. (2000). "A Causal Probabilistic Network for Optimal Treatment of Bacterial Infection." IEEE Transactions on Knowledge and Data Engineering 12(4): 517-528 325. Levitt, T. S. and K. B. Laskey (2000). "Computational Inference for Evidential Reasoning in Support of Judicial Proof." Cardozo Law Review 22: 1691-1731

326. Lewis, N. D. C. (1999). "Continuous process improvement using Bayesian belief networks." Computers & Industrial Engineering 37(1-2): 449-452 327. Li, W., P. van Beek, et al. Performing Incremental Bayesian Inference by Dynamic Model Counting http://citeseer.ist.psu.edu/li06performing.html. 328. Liang, Y. and A. G. Kelemen (2004). "Hierarchical Bayesian neural network for gene expression temporal patterns." Stat Appl Genet Mol Biol 3: Article20 329. Lin, J.-H. and P. J. Haug (2008). "Exploiting missing clinical data in Bayesian network modeling for predicting medical problems." J. of Biomedical Informatics 41(1): 1-14 330. Lindley, D. V. (1985). Making Decisions. John Wiley and Sons 331. Lindquist, M. (2007). "Use of triage strategies in the WHO signal-detection process." Drug Saf 30(7): 635-7 332. Lindquist, M., M. Stahl, et al. (2000). "A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database." Drug Saf 23(6): 533-42 333. Lipsky, A. and R. Lewis (2005). "Placing the Bayesian network approach to patient diagnosis in perspective." Ann Emerg Med 45(3): 291-4 334. Lisboa, P. J., H. Wong, et al. (2003). "A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer." Artif Intell Med 28(1): 1-25 335. Littlewood, B., L. Strigini, et al. (2000). Bayesian Belief Networks for Safety Assessment of Computer-based Systems. System Performance Evaluation Methodologies and Applications. E. Gelenbe, CRC Press, Boca Raton: 349-364. 336. Littlewood, B. and J. L. Verrall (1973). A Bayesian reliability growth model for computer software. J Royal Statist. Soc. C22, 332-34 337. Littlewood, B. and D. Wright (2007). "The Use of Multilegged Arguments to Increase Confidence in Safety Claims for Software-Based Systems: A Study Based on a BBN Analysis of an Idealized Example." Software Engineering, IEEE Transactions on 33(5): 347-365

338. Littlewood, B. and D. R. Wright (1995). A Bayesian model that combines disparate evidence for the quantitative assessment of system dependability. Proc 14th International Conference on Computer Safety (SafeComp'95), pp 173-188, Springer 339. Long, W. J., H. Fraser, et al. (1997). "Reasoning requirements for diagnosis of heart disease." Artif Intell Med 10(1): 5-24 340. Lovell, D. R., B. Rosario, et al. (1997). "Design, construction and evaluation of systems to predict risk in obstetrics." Int J Med Inform 46(3): 159-73 341. Lucas, P. F. J. (2001). Certainty-factor-like structures in Bayesian belief networks. Knowledge-Based Systems 14, 327-335 342. Lucas, P. J., L. C. van der Gaag, et al. (2004). "Bayesian networks in biomedicine and health-care." Artif Intell Med 30(3): 201-14 343. Luciani, D., S. Cavuto, et al. (2007). "Bayes pulmonary embolism assisted diagnosis: a new expert system for clinical use." Emerg Med J 24(3): 157-64 344. Luciani, D., M. Marchesi, et al. (2003). "The role of Bayesian Networks in the diagnosis of pulmonary embolism." J Thromb Haemost 1(4): 698-707 345. Luo, J., A. E. Savakis, et al. (2005). "A Bayesian network-based framework for semantic image understanding." Pattern Recognition 38(6): 919-934 346. Luu, V. T., S.-Y. Kim, et al. (2008). "Quantifying schedule risk in construction projects using Bayesian belief networks." International Journal of Project Management In Press, Corrected Proof 347. Ma, J., J. Yang, et al. (2006). "[Automatic diagnosis of malignant degree of brain glioma based on Bayesian network]." Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 23(1): 184-8 348. Ma, L., T. Arentze, et al. (2007). "Modelling land-use decisions under conditions of uncertainty." Computers, Environment and Urban Systems 31(4): 461-476

349. Ma, W., J. Beck, et al. (2006). "Bayesian inference with probabilistic population codes." Nat Neurosci 9(11): 1432-8 350. Madsen, A. L. (2007). Bayesian Networks And Influence Diagrams. New York, Springer Verlag. 351. Maglogiannis, I., E. Zafiropoulos, et al. (2006). "Risk analysis of a patient monitoring system using Bayesian Network modeling." J Biomed Inform 39(6): 637-47 352. Mahoney, S. M. and K. B. Laskey (1996). Network Engineering for Complex Belief Networks. Twelfth Conference on Uncertainty in Artificial Intelligence: 389-396 353. Mani, S. and G. F. Cooper (2004). "Causal discovery using a Bayesian local causal discovery algorithm." Medinfo 11(Pt 1): 731-5 354. Mani, S. and M. Pazzani (1998). "Guideline generation from data by induction of decision tables using a Bayesian network framework." Proc AMIA Symp: 518-22 355. Mani, S., M. Valtorta, et al. (2005). "Building Bayesian Network Models in Medicine: The MENTOR Experience." Applied Intelligence 22(2): 93-108 356. Marcot, B. G., R. S. Holthausen, et al. (2001). "Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement." Forest Ecology and Management 153(1-3): 29-42 357. Markides, K. S. and H. W. Martin (1979). "A causal model of life satisfaction among the elderly." J Gerontol 34(1): 86-93 358. Marquez, D., M. Neil, et al. (2007). Improved Dynamic Fault Tree modelling using Bayesian Networks. The 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2007. Edinburgh, IEEE 359. Marquez, D., M. Neil, et al. (2008). Solving Dynamic Fault Trees using a New Hybrid Bayesian Network Inference Algorithm. 16th Mediterranean Conference on Control and Automation (MOD 08). Ajaccio, Corsica, France 360. Marquez, D., M. Neil, et al. (2007). A new Bayesian Network approach to Reliability modelling. 5th International Mathematical Methods in Reliability Conference (MMR 07). Glasgow http://www.dcs.qmw.ac.uk/~norman/papers/Improved%20DFT%20modelling %20using%20BNs_v1.pdf. 361. Marquez, D., M. Neil, et al. (2008). Reliability Modelling Using Hybrid Bayesian Networks. ISBIS-2008 International Symposium on Business and Industrial Statistics. Prague, Czech Republic 362. Marsh, D. W. R. and G. J. Bearfield (2007). Merging Event Trees Using Bayesian Networks. ESREL 2007. Stavanger, Norway, Springer-Verlag 363. Marsh, W. and G. Bearfield (2004). Using Bayesian Networks to model accident causation in the UK railway industry. Seventh international conference PSAM. Berlin, Germany 364. Marsh, W. and G. Bearfield (2007). Representing Parameterised Fault Trees using Bayesian Networks. 26th International Conference on Computer Safety, Reliability and Security, (SAFECOMP 2007), Springer-Verlag. 365. Marshall, A. H., S. I. McClean, et al. (2001). "Developing a Bayesian belief network for the management of geriatric hospital care." Health Care Manag Sci 4(1): 25-30 366. Marshall, A. H., S. I. McClean, et al. (2002). "Modelling patient duration of stay to facilitate resource management of geriatric hospitals." Health Care Manag Sci 5(4): 313-9 367. Maskery, S. M., H. Hu, et al. (2008). "A Bayesian derived network of breast pathology co-occurrence." J. of Biomedical Informatics 41(2): 242-250 368. Mathieu, H. and L. Jean-Marc (2006). How to Orientate Arcs in a Bayesian Network Based Student Model? Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies, IEEE Computer Society: 560 - 562 369. Matsushita, Y., Y. Kuroda, et al. (2007). "Criteria revision and performance comparison of three methods of signal detection applied to the spontaneous reporting database of a pharmaceutical manufacturer." Drug Saf 30(8): 715-26

370. Matthews, R. (1998). The Cassandra Criterion. Mathematics Today (Bulletin of the IMA), 34(1), 7-10 371. Mayo, M. and A. Mitrovic (2001). "Optimising ITS Behaviour with Bayesian Networks and Decision Theory." International Journal of Artificial Intelligence 372. Mazzucchelli, R., A. Santinelli, et al. (2001). "Urothelial papillary lesions. Development of a Bayesian Belief Network for diagnosis and grading." Anticancer Res 21(2A): 1157-62 373. McKendrick, I. J., G. Gettinby, et al. (2000). "Using a Bayesian belief network to aid differential diagnosis of tropical bovine diseases." Prev Vet Med 47(3): 141-56 374. Mercier, C. and S. King (1994). "A latent variable causal model of the quality of life and community tenure of psychotic patients." Acta Psychiatr Scand 89(1): 72-7 375. Merkhofer, M. W. (1990). Using influence diagrams in multiattribute utility analysis - improving effectiveness through improving communication. Chapter 13 in 'Belief Nets and Decision Analysis', Oliver RM and Smith JQ (eds), Wiley

376. Michalowski, W., S. Wilk, et al. (2006). "Using a Bayesian belief network model to categorize length of stay for radical prostatectomy patients." Health Care Manag Sci 9(4): 341-8 377. Michel, M. and R. Jacobs (2007). "Parameter learning but not structure learning: a Bayesian network model of constraints on early perceptual learning." J Vis 7(1): 4 378. Midkiff, R. M., Jr., J. P. Burke, et al. (1989). "A causal model of mathematics performance in early adolescence: the role of sex." Psychol Rep 64(1): 167-76

379. Millard, A. V. (1994). "A causal model of high rates of child mortality." Soc Sci Med 38(2): 253-68 380. Mittal, A. and L. F. Cheong (2004). Addressing the Problems of Bayesian Network Classification of Video Using High-Dimensional Features. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 16, NO. 2 381. Mittal, A. and A. Kassim (2007). Bayesian Network Technologies: Applications and Graphical Models. 382. Mittnik, S. and I. Starobinskaya (2007). "Modeling Dependencies in Operational Risk with Hybrid Bayesian Networks." Methodol Comput Appl Probab 383. Monti, S. and G. Carenini (2000). "Dealing with the Expert Inconsistency in Probability Elicitation." IEEE Transactions on Knowledge and Data Engineering 12(4): 499-508 384. Montironi, R., P. H. Bartels, et al. (1996). "Atypical adenomatous hyperplasia (adenosis) of the prostate: development of a Bayesian belief network for its distinction from well-differentiated adenocarcinoma." Hum Pathol 27(4): 396- 407 385. Montironi, R., P. H. Bartels, et al. (1996). "Androgen-deprived prostate adenocarcinoma: evaluation of treatment-related changes versus no distinctive treatment effect with a Bayesian belief network. A methodological approach." Eur Urol 30(3): 307-15 386. Montironi, R., P. H. Bartels, et al. (1994). "Prostatic intraepithelial neoplasia. Development of a Bayesian belief network for diagnosis and grading." Anal Quant Cytol Histol 16(2): 101-12 387. Montironi, R., P. H. Bartels, et al. (1995). "Prostatic intraepithelial neoplasia (PIN). Performance of Bayesian belief network for diagnosis and grading." J Pathol 177(2): 153-62 388. Montironi, R., L. Diamanti, et al. (1997). "Subtle changes in benign tissue adjacent to prostate neoplasia detected with a Bayesian belief network." J Pathol 182(4): 442-9 389. Montironi, R., R. Mazzucchelli, et al. (2002). "Improving inter-observer agreement and certainty level in diagnosing and grading papillary urothelial neoplasms: usefulness of a Bayesian belief network." Eur Urol 41(4): 449-57

390. Montironi, R., W. F. Whimster, et al. (1996). "How to develop and use a Bayesian Belief Network." J Clin Pathol 49(3): 194-201 391. Moore, A. and D. Zuev (2005). Internet Traffic Classification using bayesian analysis techniques. SIGMETRICS '05, June 6-10, Banff, Canada 392. Morales-Napoles, O., D. Kurowicka, et al. (2008). "Continuous-discrete non- parametric Bayesian Belief Nets in Aviation Safety with UNINET." Computational Statistics and Data Analysis submitted 393. Moran, A., I. Bar-Gad, et al. (2006). "Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure." Mov Disord 21(9): 1425-31 394. Morelli, R., J. Bronzino, et al. (1987). "Expert systems in psychiatry. A review." J Med Syst 11(2-3): 157-68 395. Morgan, B. W. (1968). An Introduction to Bayesian Statistical Decision Processes. 396. Morris, S. B., D. Cork, et al. (1997). "The cognitive processing of causal knowledge." 397. Morrison, M., W. McCluggage, et al. (2002). "Expert system support using a Bayesian belief network for the classification of endometrial hyperplasia." J Pathol 197(3): 403-14 398. Mortera, J., A. P. Dawid, et al. (2003). "Probabilistic expert systems for DNA mixture profiling." Theor. Pop. Biol 63: 191-205 399. Moses, J. and M. Farrow (2003). A Procedure for Assessing the Influence of Problem Domain on Effort Estimation Consistency. Software Quality J, 11, 283- 300 400. Mossman, D. (2000). "Interpreting clinical evidence of malingering: a Bayesian perspective." J Am Acad Psychiatry Law 28(3): 293-302 401. Muller, P. and D. R. Insua (1998). "Issues in Bayesian Analysis of Neural Network Models." Neural Comput 10(3): 749-70 402. Murad, N. (2004). Research into and Devlepment of an Infrastructure Risk management Model and Related Processes (with Eli Lilly and Co Ltd). MSc, University of Surrey, Dept of Computing 403. Murphy, K. (1998 ). "A Brief Introduction to Graphical Models and Bayesian Networks." 404. http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html. 405. Murphy, K. (1999). A variational approximation for Bayesian networks with discrete and continuous latent variables. Uncertainty in AI, 15. K. Laskey and H. Prade: 467-475 406. Murphy, K. P. (2001). "The Bayes net toolbox for Matlab." Computing Science and Statistics 33 http://www.cs.berkeley.edu/ murphyk/Bayes/bnt.html. 407. Nasir, D., B. McCabe, et al. (2003). "Evaluating Risk in Construction–Schedule Model (ERIC–S) Construction Schedule Risk Model." Journal of Construction Engineering and Management 129(5): 518-527 408. Neapolitan, R. E. (2004). Learning Bayesian Networks, Prentice Hall. 409. Neff, J. A. and S. L. Crawford (1998). "The Health Belief Model and HIV risk behaviours: a causal model analysis among Anglos, African-Americans and Mexican-Americans." Ethn Health 3(4): 283-99 410. Negrín, M. and F. Vázquez-Polo (2006). "Bayesian cost-effectiveness analysis with two measures of effectiveness: the cost-effectiveness acceptability plane." Health Econ 15(4): 363-72 411. Neil, M. and N. Fenton (2005). Improved Methods for building large-scale Bayesian Networks. The Third Bayesian Modeling Applications Workshop, Uncertainty in Artificial Intelligence (UAI) 2005, Edinburgh University. 412. Neil, M., N. Fenton, et al. (2003). Assessing Vehicle Reliability using Bayesian Networks. Global Vehicle Reliability J. E. Strutt and P. L. Hall, Professional Engineering Publishing: 25-42. 413. Neil, M., N. Fenton, et al. (2007). Using Bayesian Networks and Simulation for Data Fusion and Risk Analysis. NATO Science for Peace and Security Series: Information and Communication Security. Skanata and D. M. Byrd, IOS Press, Nieuwe Hemweg 6B, 1013 BG Amsterdam, The Netherlands. 13. 414. Neil, M., N. Fenton, et al. (2000). "Building large-scale Bayesian Networks." The Knowledge Engineering Review, 15(3) 15(3): 257-284 415. Neil, M., N. Fenton, et al. (2005). "Using Bayesian networks to model expected and unexpected operational losses." RISK ANALYSIS 25(4): 963-972 416. Neil, M. and N. E. Fenton (1996). Predicting software quality using Bayesian belief networks. Proc 21st Annual Software Eng Workshop. V. Basili. NASA Goddard Space Flight Centre: 217-230 417. Neil, M. and N. E. Fenton (1999). Applying Bayesian Belief Networks to Critical Systems Assessment. Safety Critical Systems Club Newsletter, Centre for Software Reliability. 8: 10-13 418. Neil, M. and N. E. Fenton (2001). EPSRC IMPRESS (IMproving the software PRocESS using Bayesian Networks) project final report, Queen Mary University of London http://www.dcs.qmul.ac.uk/research/radar/Projects/impress/impress %20igr.html. 419. Neil, M. and N. E. Fenton (2003). Process Modelling and object oriented Bayesian nets 420. Neil, M., N. E. Fenton, et al. (2003). Modelling subjective causes and objective consequences in Bayesian Networks. RADAR Tech Report TR101 421. Neil, M., N. E. Fenton, et al. (2001). "Using Bayesian Belief Networks to Predict the Reliability of Military Vehicles." IEE Computing and Control Engineering Journal 12(1): 11-20 422. Neil, M., P. Krause, et al. (2003). Software Quality Prediction Using Bayesian Networks. Software Engineering with Computational Intelligence. T. M. Khoshgoftaar, Kluwer. 423. Neil, M., B. Littlewood, et al. (1996). Applying Bayesian belief networks to systems dependability assessment. Safety-Critical Systems: the Convergence Of High Tech and Human Factors; Proceedings of the 4th Safety Critical Systems Symposium. F. Redmill, Springer Verlag: 71-93. 424. Neil, M., B. Malcolm, et al. (2003). Modeling an Air Traffic Control Environment Using Bayesian Belief Networks. 21st International System Safety Conference Ottawa, Ontario, Canada 425. Neil, M., D. Marquez, et al. (2008). "Using Bayesian Networks to Model the Operational Risk to Information Technology Infrastructure in Financial Institutions." Journal of Financial Transformation 22: 131-138 426. Neil, M., M. Tailor, et al. (2006). Modeling Dependable Systems using Hybrid Bayesian Networks. First International Conference on Availability, Reliability and Security (ARES'06) IEEE Computer Society: 817-823 http://doi.ieeecomputersociety.org/10.1109/ARES.2006.83. 427. Neil, M., M. Tailor, et al. (2007). "Inference in hybrid Bayesian networks using dynamic discretization." Statistics and Computing 17(3): 219-233 http://dx.doi.org/10.1007/s11222-007-9018-y. 428. Neil, M., M. Tailor, et al. (2008). "Modelling dependable systems using hybrid Bayesian networks." Reliability Engineering and System Safety 93(7): 933-939

429. Newton, A. C., E. Marshall, et al. (2006). "Use of a Bayesian belief network to predict the impacts of commercializing non-timber forest products on livelihoods." Ecology and Society 11(2): 24 430. Newton, A. C., G. B. Stewart, et al. (2007). "Bayesian Belief Networks as a tool for evidence-based conservation management." Journal for Nature Conservation 15(2): 144-160 431. Ng, C. M. (2003). "Comparison of neural network, Bayesian, and multiple stepwise regression-based limited sampling models to estimate area under the curve." Pharmacotherapy 23(8): 1044-51 432. Nguyen, S. T., H. T. Nguyen, et al. (2006). "Improved Head Direction Command Classification using an Optimised Bayesian Neural Network." Conf Proc IEEE Eng Med Biol Soc 1: 5679-82 433. Niedermayer, D. An Introduction to Bayesian Networks and their Contemporary Applications. http://www.gpfn.sk.ca/ 434. Nikiforidis, G. and G. Sakellaropoulos (1998). "Expert system support using Bayesian belief networks in the prognosis of head-injured patients of the ICU." Med Inform (Lond) 23(1): 1-18 435. Nikolajewa, S., R. Pudimat, et al. (2007). "BioBayesNet: a web server for feature extraction and Bayesian network modeling of biological sequence data." Nucleic Acids Res 35(Web Server issue): W688-93 436. Nikovski, D. (2000). "Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistics." IEEE Transactions on Knowledge and Data Engineering 12(4): 509-518 437. Nordmann, J. P. and G. Berdeaux (2007). "Use of a bayesian network to predict the nighttime intraocular pressure peak from daytime measurements." Clin Ther 29(8): 1751-60 438. Nouraei, S. A., Q. J. Huys, et al. (2007). "Screening patients with sensorineural hearing loss for vestibular schwannoma using a Bayesian classifier." Clin Otolaryngol 32(4): 248-54 439. Nyberg, J. B., B. G. Marcot, et al. (2006). "Using Bayesian belief networks in adaptive management." Canadian Journal of Forest Research 36: 3104-3116 440. Oatley, G. C. and B. W. Ewart (2003). "Crimes analysis software: ‘pins in maps’, clustering and Bayes net prediction." Expert Systems with Applications 25(4): 569-588 441. Ogawa, N. and R. W. Hodge (1983). "Towards a causal model of childbearing and abortion attitudes in contemporary Japan." Popul Res Leads(No. 15): 1-32

442. Olesen, K. G. (1993). "Causal probabilistic networks with both discrete and continuous variables." 443. Oliver, R. M. and J. Q. Smith (1990). Belief Nets and Decision Analysis. J Wiley & Sons 444. Olson, J. T., J. W. Rozenblit, et al. (2007). "Hardware/Software Partitioning Using Bayesian Belief Networks." Systems, Man and Cybernetics, Part A, IEEE Transactions on 37(5): 655-668 445. Onisko, A., M. J. Druzdzel, et al. (2000). Learning Bayesian Network Parameters from Small Data Sets: Application of Noisy-OR Gates. In Workshop on � Bayesian and Causal Networks: From Inference to Data Mining, 12th European Conference on Artificial Intelligence (ECAI-2000), Berlin 446. Onisko, A., M. J. Durzdel, et al. (1998). "A probabilistic causal model for diagnosis of liver disorders." 447. Orre, R., A. Bate, et al. (2005). "A bayesian recurrent neural network for unsupervised pattern recognition in large incomplete data sets." Int J Neural Syst 15(3): 207-22 448. Padman, R., X. Bai, et al. (2007). "A new machine learning classifier for high dimensional healthcare data." Stud Health Technol Inform 129: 664-8 449. Pang, B. C., V. Kuralmani, et al. (2007). "Hybrid outcome prediction model for severe traumatic brain injury." J Neurotrauma 24(1): 136-46 450. Paraskevis, D., E. Magiorkinis, et al. (2004). "Phylogenetic reconstruction of a known HIV-1 CRF04_cpx transmission network using maximum likelihood and Bayesian methods." J Mol Evol 59(5): 709-17 451. Patel, R., F. Bowman, et al. (2006). "A Bayesian approach to determining connectivity of the human brain." Hum Brain Mapp 27(3): 267-76 452. Pearl, J. (1986). "Fusion, propagation, and structuring in belief networks." 453. Pearl, J. (1988). Probabilistic reasoning in intelligent systems. 454. Pearl, J. (1995). "Causal inference from indirect experiments." Artif Intell Med 7(6): 561-82 455. Pearl, J. (2000). Causality: Models Reasoning and Inference, Cambridge University Press. 456. Peek, N., M. Verduijn, et al. (2007). "Bayesian networks for multivariate data analysis and prognostic modelling in cardiac surgery." Stud Health Technol Inform 129: 596-600 457. Pe'er, D. (2005). "Bayesian network analysis of signaling networks: a primer." Sci STKE 2005(281): pl4 458. Pena, J. M., J. Bjorkegren, et al. (2005). "Growing Bayesian network models of gene networks from seed genes." Bioinformatics 21 Suppl 2: ii224-9 459. Pendharkar, P. C., G. H. Subramanian, et al. (2005). "A Probabilistic Model for Predicting Software Development Effort." IEEE Transactions on Software Engineering 31(7): 615-624 460. Peng, S. Y., K. C. Wu, et al. (2007). "Predicting postoperative nausea and vomiting with the application of an artificial neural network." Br J Anaesth 98(1): 60-5 461. Penny, W. D. and S. J. Roberts (1999). "Bayesian neural networks for classification: how useful is the evidence framework?" Neural Netw 12(6): 877- 892 462. Pérez-Miñana, E. and J.-J. Gras (2006). "Improving fault prediction using Bayesian networks for the development of embedded software applications: Research Articles." Softw. Test. Verif. Reliab. 16(3): 157-174 http://dx.doi.org/10.1002/stvr.v16:3. 463. Petersen, M. L., S. E. Sinisi, et al. (2006). "Estimation of direct causal effects." Epidemiology 17(3): 276-84 464. Peyrot, M. (1996). "Causal analysis: theory and application." J Pediatr Psychol 21(1): 3-24 465. Phillips, L. D., P. Humphreys, et al. (1990). A socio-technical approach to assessing human reliability. Chapter 11 in 'Belief Nets and Decision Analysis', Oliver RM and Smith JQ (eds), Wiley 466. Pietz, K., M. M. Byrne, et al. (2006). "A decision-theoretic approach to identifying future high-cost patients." Med Care 44(9): 842-9 467. Polley, M. J., D. A. Winkler, et al. (2004). "Broad-based quantitative structure- activity relationship modeling of potency and selectivity of farnesyltransferase inhibitors using a Bayesian regularized neural network." J Med Chem 47(25): 6230-8 468. Pourret, O., P. Naim, et al., Eds. (2008). Bayesian Networks: A Practical Guide to Applications (Statistics in Practice), John Wiley and Sons Ltd 469. Price, G. J., W. G. McCluggage, et al. (2003). "Computerized diagnostic decision support system for the classification of preinvasive cervical squamous lesions." Hum Pathol 34(11): 1193-203 470. Price, J. L. and C. W. Mueller (1981). "A causal model for turnover for nurses." Acad Manage J 24(3): 543-65 471. Radliński, Ł., N. E. Fenton, et al. (2007). Improved Decision-Making for Software Managers Using Bayesian Networks. 11th IASTED Int. Conf. Software Engineering and Applications (SEA). Cambridge, MA, USA 13–19

472. Radliński, Ł., N. E. Fenton, et al. (2007). "Modelling Prior Productivity and Defect Rates in a Causal Model for Software Project Risk Assessment." Polish Journal of Environmental Studies 16(4A): 256-260 473. Ramoni, M. and P. Sebastiani (1998). Bayesian Methods for Intelligent Data Analysis. Knowledge Media Institute, The Open University, KMI-TR-67 474. Ramoni, M. and P. Sebastiani (1998). Parameter Estimation in Bayesian Networks from Incomplete Databases. Intelligent Data Analysis, 2(1) 475. Ratcliffe, B. and A. L. Rollo (1990). "Adapting function point analysis to Jackson System Development." 476. Raval, A., Z. Ghahramani, et al. (2002). "A Bayesian network model for protein fold and remote homologue recognition." Bioinformatics 18(6): 788-801 477. Reynolds, G. M., A. C. Peet, et al. (2007). "Generating prior probabilities for classifiers of brain tumours using belief networks." BMC Med Inform Decis Mak 7(1): 27 478. Riviere, C., P. Lauret, et al. (2006). "A Bayesian Neural Network approach to estimating the Energy Equivalent Speed." Accid Anal Prev 38(2): 248-59 479. Robertson, B. and T. Vignaux (1995). Interpreting Evidence: Evaluating Forensic Science in the Courtroom, John Wiley and Son Ltd. 480. Robertson, B. and T. Vignaux. (2007). "The Bayesian Evidence Page." 481. http://www.mcs.vuw.ac.nz/~vignaux/evidenceindex.html. 482. Robins, J. M. and S. Greenland (1986). "The role of model selection in causal inference from nonexperimental data." Am J Epidemiol 123(3): 392-402 483. Robles, V., P. Larrañaga, et al. (2004). "Bayesian network multi-classifiers for protein secondary structure prediction." Artif Intell Med 31(2): 117-36 484. Rothman, K. J. and S. Greenland (2005). "Causation and causal inference in epidemiology." Am J Public Health 95 Suppl 1: S144-50 485. Sabatti, C. and L. Rohlin (2004). "A Bayesian approach to expression network component analysis." Conf Proc IEEE Eng Med Biol Soc 4: 2933-6 486. Sachs, K., D. Gifford, et al. (2002). "Bayesian network approach to cell signaling pathway modeling." Sci STKE 2002(148): PE38 487. Sakellaropoulos, G. C. and G. C. Nikiforidis (1999). "Development of a Bayesian Network for the prognosis of head injuries using graphical model selection techniques." Methods Inf Med 38(1): 37-42 488. Sakellaropoulos, G. C. and G. C. Nikiforidis (2000). Prognostic performance of two expert systems based on Bayesian belief networks. Decision Support Systems 27, 431-442 489. Saks, M. J. and W. C. Thompson (2003). Assessing Evidence: Proving Facts. Handbook of Psychology in Legal Contexts (Second Edition). D. Carson and R. Bull. 490. Sandberg, A., A. Lansner, et al. (2002). "A Bayesian attractor network with incremental learning." Network 13(2): 179-94 491. Sanders, D. and D. Aronsky (2006). "Detecting asthma exacerbations in a pediatric emergency department using a Bayesian network." AMIA Annu Symp Proc: 684-8 492. Sanders, D. and D. Aronsky (2006). "Prospective evaluation of a Bayesian Network for detecting asthma exacerbations in a Pediatric Emergency Department." AMIA Annu Symp Proc: 1085 493. Sanders, D. L. and D. Aronsky (2006). "Detecting asthma exacerbations in a pediatric emergency department using a Bayesian network." AMIA Annu Symp Proc: 684-8 494. Schachter, A. D., M. F. Ramoni, et al. (2007). "Economic evaluation of a bayesian model to predict late-phase success of new chemical entities." Value Health 10(5): 377-85 495. Schaffner, K. F. (1993). "Clinical trials and causation: Bayesian perspectives." Stat Med 12(15-16): 1477-94; discussion 1495-9 496. Schmitt, S. A. (1969). Measuring Uncertainty: An Elementary Introduction to Bayesian Statistics. 497. Schriger, D., L. Baraff, et al. (1995). "EDECS: the Emergency Department Expert Charting System." Medinfo 8 Pt 2: 1665 498. Schurink, C., P. Lucas, et al. (2005). "Computer-assisted decision support for the diagnosis and treatment of infectious diseases in intensive care units." Lancet Infect Dis 5(5): 305-12 499. Schwartz, A. R., W. Gerin, et al. (2003). "Toward a causal model of cardiovascular responses to stress and the development of cardiovascular disease." Psychosom Med 65(1): 22-35 500. Sebastiani, P., V. G. Nolan, et al. (2007). "A network model to predict the risk of death in sickle cell disease." Blood 110(7): 2727-35 501. Shaffer, M. L. and V. M. Chinchilli (2004). "Bayesian inference for randomized clinical trials with treatment failures." Stat Med 23(8): 1215-28 502. Shek, T. W. (1996). "Bayesian Belief Network in histopathology." J Clin Pathol 49(10): 864 503. Shenoy, P. and G. Shafer (1990). Axioms for probability and belief-function propagation. Readings in uncertain reasoning, Morgan Kaufmann Publishers Inc: 575 - 610. 504. Sierra, B. and P. Larranaga (1998). "Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches." Artif Intell Med 14(1-2): 215-30 505. Sierra, B., N. Serrano, et al. (2001). "Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data." Artif Intell Med 22(3): 233-48 506. Sigurdsson, J. H., L. A. Walls, et al. (2001). "Bayesian belief nets for managing expert judgement and modelling reliabilit." Quality and Reliability Engineering International 17(3 ): 181 - 190 507. Simon, C., P. Weber, et al. (2007). "Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis." Reliab Eng Syst Safety 10.1016/j.ress.2007.03.012. 508. Simon, C., P. Weber, et al. (2007). "Bayesian Networks and Evidence Theory to Model Complex Systems." Reliability Journal of Computers 2(1): 33-43 509. Singh, M. and M. Valtorta (1995). "Construction of Bayesian network structures from data: a brief survey and an efficient algorithm." International Journal of Approximate Reasoning (IJAR) 12: 111-131 510. Skaanning, C. (2000). A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters. Sixteenth Conference on Uncertainty in Artificial Intelligence. Stanford, CA: Stanford University 511. Skaanning, C. (2003). Automated diagnosis of printer systems using Bayesian networks, US Patent 6535865 www.patentstorm.us/patents/6535865.html. 512. Skaanning, C., F. J. Jensen, et al. (2000). Printer Troubleshooting Using Bayesian Networks. LECTURE NOTES IN COMPUTER SCIENCE, ISSU 1821, 367-379. 513. Smets, P. (1993). "Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem." Int J Approx Reason 9(1): 1-35 514. Smets, P. and K. R. (1994). "The transferable belief model." Artif Intell Med 66: 191-243 515. Smith, C. S., A. L. Howes, et al. (2007). "Using a Bayesian belief network to predict suitable habitat of an endangered mammal – The Julia Creek dunnart (Sminthopsis douglasi)." Biological Conservation 139(3-4): 333-347 516. Smith, J. Q. and J. Croft (2003). "Bayesian networks for discrete multivariate data: an algebraic approach to inference." J. of Multivariate Analysis 84: 387- 402 517. Spiegelhalter, D., A. Dawid, et al. (1993). "Bayesian analysis in expert systems." Stat Sci 8: 219-83 518. Spiegelhalter, D. J., A. P. Dawid, et al. (1991). "Probabilistic expert systems and graphical modelling: a case study in drug safety." Phil. Trans. R. Soc. Lond. 337: 387-405 519. Spiegelhalter, D. J. and S. L. Lauritzen (1990). "Sequential Updating of Conditional Probabilities on Directed Graphical Structure." NETWORKS 20: 579-606 520. Spirtes, P., C. Glymour, et al. (2000). Causation, Prediction, and Search, Adaptive Computation and Machine Learning. Cambridge, Massachusetts, MIT Press. 521. Spirtes, P. L. (2002). Data mining tasks and methods: Probabilistic and casual networks: methodology for probabilistic networks, Handbook of data mining and knowledge discovery. New York, NY, Oxford University Press, Inc., . 522. Srinivas, S. (1993). A Generalization of the Noisy-Or Model. Ninth Conference on Uncertainty in Artificial Intelligence: 208-218 523. Stamelosa, I., L. Angelisa, et al. (2005). "On the use of Bayesian belief networks for the prediction of software productivity." Information and Software Tech 45 (1): 51-60 524. Stiber, N., M. Small, et al. (2004). "Site-specific updating and aggregation of Bayesian belief network models for multiple experts." Risk Anal 24(6): 1529-38

525. Suebnukarn, S. and P. Haddawy (2006). "A Bayesian approach to generating tutorial hints in a collaborative medical problem-based learning system." Artif Intell Med 38(1): 5-24 526. Suermondt, H. J. (1992). Explanation in Bayesian Belief Networks. Department of Computer Science and Medicine Stanford, CA, Stanford University. PhD 527. Sun, W., G. M. Zeng, et al. (2005). "Bayesian regularized BP neural network model for quantitative relationship between the electrochemical reduction potential and molecular structures of chlorinated aromatic compounds." Huan Jing Ke Xue 26(2): 21-7 528. Suzuki, J. (1996). Learning Bayesian belief networks based on the MDL principle: An efficient algorithm using the branch and bound technique. Proceedings of the international conference on machine learning. Bally, Italy

529. Suzuki, K., H. Yoshida, et al. (2006). "Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: Suppression of rectal tubes." Med Phys 33(10): 3814-24 530. Tailor, M., M. Neil, et al. (2003). Bayesian Net propagation algorithms 531. Tamada, Y., H. Bannai, et al. (2005). "Utilizing evolutionary information and gene expression data for estimating gene networks with bayesian network models." J Bioinform Comput Biol 3(6): 1295-313 532. Tamada, Y., S. Kim, et al. (2003). "Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection." Bioinformatics 19 Suppl 2: ii227-36 533. Tang, A., A. Nicholson, et al. (2007). "Using Bayesian belief networks for change impact analysis in architecture design 534. " Journal of Systems and Software 80(1): 127-148 535. Tang, Z., M. J. Taylor, et al. (2005). "Quantitative risk modelling for new pharmaceutical compounds." Drug Discov Today 10(22): 1520-6 536. Taroni, F., C. Aitken, et al. (2006). Bayesian Networks and Probabilistic Inference in Forensic Science John Wiley & Sons. 537. Taroni, F. and A. Biedermann (2005). Inadequacies of posterior probabilities for the assessment of scientific evidence. Law Probablity and Risk 4: 89-114 538. Taroni, F., A. Biedermann, et al. (2004). "A general approach to Bayesian networks for the interpretation of evidence." Forensic Science International 139(1): 5-16 http://www.sciencedirect.com/science/article/B6T6W- 4B70FWS-2/2/00e9c4977552133141d6728478399ba9. 539. Taroni, F., S. Bozza, et al. (2005). "Decision Analysis in forensic science." Journal of Forensic Sciences 50: 894-905 540. Taroni, F., S. Bozza, et al. (2006). "Two items of evidence, no putative source: an inference problem in forensic intelligence." J Forensic Sci 51(6): 1350-61 541. Tattari, S., T. Schultz, et al. (2003). "Use of belief network modelling to assess the impact of buffer zones on water protection and biodiversity." Agriculture, Ecosystems & Environment 96(1-3): 119-132 542. Tepes, B., L. Szirovicza, et al. (2005). "Causal Bayesian network for tagging syntactical structure of Croatian sentences." Coll Antropol 29(2): 731-3 543. The Economist (2001). Son of paperclip, 22 March 2001 http://www.cs.ubc.ca/~murphyk/Bayes/econ.22mar01.html. 544. Thomas, A., D. J. Spiegelhalter, et al. (1992). BUGS: A program to perform Bayesian inference using Gibbs sampling. Bayesian Statistics, . J. M. Bernardo, J.O. Berger, A. P. Dawid and A. F. Smith, University Press, Oxford. 4: 837-842. 545. Thompson, W. C. and E. L. Schumann (1987). "Interpretation of statistical evidence in criminal trials." Law and Human Behavior 11(3): 167-187 http://dx.doi.org/10.1007/BF01044641. 546. Tian, L. and A. Noore (2004). "Software reliability prediction using recurrent neural network with Bayesian regularization." Int J Neural Syst 14(3): 165-74

547. Ticehurst, J. L., L. H. Newham, et al. (2007). "A Bayesian network approach for assessing the sustainability of coastal lakes in New South Wales, Australia." Environ. Model. Softw. 22(8): 1129-1139 548. Tienda-Luna, I. M., Y. Yin, et al. (2007). "Inferring the skeleton cell cycle regulatory network of malaria parasite using comparative genomic and variational Bayesian approaches." Genetica 549. Tong, S. and D. Koller (2001). "Active Learning for Parameter Estimation in Bayesian Networks." ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS ISSU 13: 647-653 550. Tonglet, R., M. Mudosa, et al. (1992). "The causal model approach to nutritional problems: an effective tool for research and action at the local level." Bull World Health Organ 70(6): 715-23 551. Trucco, P., E. Cagnoa, et al. (2007). "A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime." Reliability Engineering & System Safety 93(6): 845-856 552. Tucker, A., V. Vinciotti, et al. (2005). "A spatio-temporal Bayesian network classifier for understanding visual field deterioration." Artif Intell Med 34(2): 163-77 553. Tudor, R. S., R. Hovorka, et al. (1998). "DIAS-NIDDM--a model-based decision support system for insulin dose adjustment in insulin-treated subjects with NIDDM." Comput Methods Programs Biomed 56(2): 175-91 554. Twardy, C., A. E. Nicholson, et al. (2006). "Epidemiological data mining of cardiovascular Bayesian networks." Electronic Journal of Health Informatics 1(1): 1-13 http://www.ejhi.net. 555. Van den Hurk, M. M., D. H. Dolmans, et al. (2001). "Testing a causal model for learning in a problem-based curriculum." Adv Health Sci Educ Theory Pract 6(2): 141-9 556. van der Gaag, L. C., S. Renooij, et al. (2002). "Probabilities for a Probabilistic Network: A Case-study in Oesophageal Cancer." Artif Intell Med 25(2): 123-48

557. van Engelen, R. A. (1997). "Approximating Bayesian belief networks by arc removal." Pattern Analysis and Machine Intelligence, IEEE Transactions on 19(8): 916-920 558. van Koten, C. and A. R. Gray (2006). "Bayesian statistical effort prediction models for data-centred 4GL software development." Information and Software Technology 48(11 ): 1056-1067 559. Varis, O. (1998). "A belief network approach to parameter estimation: application to resource and environmental management." Artificial Intelligence 101: 135-163 560. Verduijn, M., P. M. Rosseel, et al. (2007). "Prognostic Bayesian networks I: Rationale, learning procedure, and clinical use." J Biomed Inform 561. Verduijn, M., P. M. Rosseel, et al. (2007). "Prognostic Bayesian networks II: An application in the domain of cardiac surgery." J Biomed Inform 562. Vineis, P. and D. Kriebel (2006). "Causal models in epidemiology: past inheritance and genetic future." Environ Health 5: 21 563. Vivarelli, F. and C. K. Williams (2001). "Comparing Bayesian neural network algorithms for classifying segmented outdoor images." Neural Netw 14(4-5): 427-37 564. Vomlel, J. (2004). "Bayesian networks in educational testing." Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 12(SUPPLEMENT): 83-100 565. Waldmann, M. R. (2000). "Competition among causes but not effects in predictive and diagnostic learning." Journal of experimental psychology: Learning, memory and cognition 26(1): 53-76 566. Waldmann, M. R. (2001). "Predictive versus diagnostic causal learning: evidence from an overshadowing paradigm." Psychonomic Bulletin & Review 8: 600-608 567. Waldmann, M. R. and L. Martignon (1999). A Bayesian network model of causal learning. Proceeding of the 20th annual conference of the Cognitive Science Society. M. A. Gernsbasher and S. J. Derry. Mahwah, NJ, Erlbaum 568. Wang, H. and M. J. Druzdel User interface tools for navigation inc onditional probability tables and eliciation of probabilities in Bayesian networks. School of Information Sciences & Intelligent Systems, University of Pittsburgh, PA 15260, www.sis.pitt.edu/ 569. Wang, H., F. Peng, et al. (2006). Software Project Level Estimation Model Framework based on Bayesian Belief Networks. Sixth International Conference on Quality Software (QSIC'06) 209-218 570. Wang, M., Z. Chen, et al. (2007). "A hybrid Bayesian network learning method for constructing gene networks." Comput Biol Chem 31(5-6): 361-72 571. Wang, X. H., B. Zheng, et al. (1999). "Computer-assisted diagnosis of breast cancer using a data-driven Bayesian belief network." Int J Med Inform 54(2): 115-26 572. Wang, Y. H., Y. Li, et al. (2005). "An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network." J Comput Aided Mol Des 19(3): 137-47 573. Washington, S. and J. Oh (2006). "Bayesian methodology incorporating expert judgment for ranking countermeasure effectiveness under uncertainty: example applied to at grade railroad crossings in Korea." Accid Anal Prev 38(2): 234-47

574. Wasyluk, H., A. Oniśko, et al. (2001). "Support of diagnosis of liver disorders based on a causal Bayesian network model." Med Sci Monit 7 Suppl 1: 327-32

575. Watthayu, W. and Y. Peng (2004). A Bayesian Network Based Framework for Multi-Criteria Decision-Making MCDM 2004. Whistler, B. C. Canada 576. Weber, P. and L. Jouffe (2006). "Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN)." Reliability Engineering and System Safety 91(2): 149-162 577. Weber, P., M. P, et al. (2004). Dynamic Bayesian Networks modelling the dependability of systems with degradations and exogenous constraints. 11th IFAC symposium on information control problems in manufacturing (INCOM’04). Salvador-Bahia, Brazil 578. Weber, P. and M.-C. Suhner (2002). An application of Bayesian Networks to the Performance Analysis of a Process European Conference on System Dependability and Safety (ESRA 2002/lambda-Mu13). Lyon, France 579. Weber, P., M.-C. Suhner, et al. (2001). System approach-based Bayesian Network to aid maintenance of manufacturing process. 6th IFAC Symposium on Cost Oriented Automation, Low Cost Automation. Germany 580. Weber, P., D. Theilliol, et al. (2006). .Increasing effectiveness of model-based fault diagnosis: A Dynamic Bayesian Network design for decision making. 6th IFAC Symposium on Fault Detection, Supervision and Safety of technical processes. China 581. Wellman, M. P. (1990). "Fundamental concepts of qualitative probabilistic networks." 582. Whimster, W. F., P. W. Hamilton, et al. (1996). "Reproducibility of Bayesian belief network assessment of breast fine needle aspirates." Anal Quant Cytol Histol 18(4): 267-74 583. White, S. (1996). "Decision-support systems in dentistry." J Dent Educ 60(1): 47-63 584. Williams, S. J., S. M. Shortell, et al. (1978). "A causal model of health services for diabetic patients." Med Care 16(4): 313-26 585. Wilson, A. G. and A. V. Huzurbazar (2007). "Bayesian networks for multilevel system reliability 586. " Reliability Engineering & System Safety 92(10): 1413-1420 587. Wilson, W. I., Y. Peng, et al. (2005). "Comparison of statistical analysis and Bayesian Networks in the evaluation of dissolution performance of BCS Class II model drugs." J Pharm Sci 94(12): 2764-76 588. Winkler, R. L. (1972). An Introduction to Bayesian Inference and Decision. 589. Winkler, R. L. (2001). "WHY BAYESIAN ANALYSIS HASN'T CAUGHT ON IN HEALTHCARE DECISION MAKING." International Journal of Technology Assessment in Health Care 17: 56-66 590. Wolbrechte, Bruced'Ambrosio, et al. (2000). Monitoring and diagnosis of a multistage manufacturing process using Bayesian networks. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 14 (1), 53-67.

591. Wolfle, L. M. and L. W. Bryant (1978). "A causal model of nursing education and state board examination scores." Nurs Res 27(5): 311-5 592. Wong, W. K., G. Cooper, et al. (2005). "Use of multiple data streams to conduct Bayesian biologic surveillance." MMWR Morb Mortal Wkly Rep 54 Suppl: 63-9

593. Wooff, D. A., M. Goldstein, et al. (2002). Bayesian graphical models for software testing. IEEE Transactions on Software Engineering, 28(5), 510 - 525

594. Wooff, D. A. and J. M. Schneider (2006). "A Bayesian belief network for quality assessment: application to employment officer support." J Intellect Disabil Res 50(Pt 2): 109-26 595. Xiang, Z., R. M. Minter, et al. (2007). "miniTUBA: medical inference by network integration of temporal data using Bayesian analysis." Bioinformatics 23(18): 2423-32 596. Xie, Y., D. Lord, et al. (2007). "Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis." Accid Anal Prev 39(5): 922-33 597. Xu, M., G. M. Zeng, et al. (2005). "Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-a case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake." J Environ Sci (China) 17(6): 946-52 598. Yu, J. and X. W. Chen (2005). "Bayesian neural network approaches to ovarian cancer identification from high-resolution mass spectrometry data." Bioinformatics 21 Suppl 1: i487-94 599. Yu, J., V. A. Smith, et al. (2004). "Advances to Bayesian network inference for generating causal networks from observational biological data." Bioinformatics 20(18): 3594-603 600. Yuan, C. and M. J. Druzdzel (2005). "Importance sampling algorithms for Bayesian networks: Principles and performance." Mathematical and Computer Modeling 43(9-10): 1189-1207 601. Zelic, I., I. Kononenko, et al. (1997). "Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries." J Med Syst 21(6): 429-44

602. Zhang, R., G. McAllister, et al. (2005). "Coupling wavelet transform with bayesian network to classify auditory brainstem responses." Conf Proc IEEE Eng Med Biol Soc 7: 7568-71 603. Zhou, Z., G. Jin, et al. (2006). Reliability Analysis of Multistate Systems Based on Bayesian Networks. Proceedings of the 13th Annual IEEE International Symposium and Workshop on Engineering of Computer Based Systems, IEEE Computer Society: 344-352 604. Zhu, J., A. Jambhekar, et al. (2006). "A Bayesian Network Driven Approach to Model the Transcriptional Response to Nitric Oxide in Saccharomyces cerevisiae." PLoS ONE 1: e94 605. Zhu, W., J. Yan, et al. (2006). "Application of Bayesian network in syndrome differentiation system of traditional Chinese medicine." Zhong Xi Yi Jie He Xue Bao 4(6): 567-71 606. Ziv, H. and D. J. Richardson (1997). Bayesian-network confirmation of software testing uncertainties. ESEC 607. Zou, M. and S. Conzen (2005). "A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data." Bioinformatics 21(1): 71-9 608. Zweig, G. (1998). Speech Recognition with Dynamic Bayesian Networks, University of California at Berkeley. PhD http://www.icsi.berkeley.edu/ftp/global/pub/speech/papers/zweig_thesis.pdf.

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