DOCSLIB.ORG

1 Global Sensitivity and Uncertainty Analysis Of

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
1 Global Sensitivity and Uncertainty Analysis Of GLOBAL SENSITIVITY AND UNCERTAINTY ANALYSIS OF SPATIALLY DISTRIBUTED WATERSHED MODELS By ZUZANNA B. ZAJAC A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010 1 © 2010 Zuzanna Zajac 2 To Król Korzu KKMS! 3 ACKNOWLEDGMENTS I would like to thank my advisor Rafael Muñoz-Carpena for his constant support and encouragement over the past five years. I could not have achieved this goal without his patience, guidance, and persistent motivation. For providing innumerable helpful comments and helping to guide this research, I also thank my graduate committee co-chair Wendy Graham and all the members of the graduate committee: Michael Binford, Greg Kiker, Jayantha Obeysekera, and Karl Vanderlinden. I would also like to thank Naiming Wang from the South Florida Water Management District (SFWMD) for his help understanding the Regional Simulation Model (RSM), the great University of Florida (UF) High Performance Computing (HPC) Center team for help with installing RSM, South Florida Water Management District and University of Florida Water Resources Research Center (WRRC) for sponsoring this project. Special thanks to Lukasz Ziemba for his help writing scripts and for his great, invaluable support during this PhD journey. To all my friends in the Agricultural and Biological Engineering Department at UF: thank you for making this department the greatest work environment ever. Last, but not least, I would like to thank my father for his courage and the power of his mind, my mother for the power of her heart, and my brother for always being there for me. 4 TABLE OF CONTENTS Page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 8 LIST OF FIGURES .......................................................................................................... 9 LIST OF ABBREVIATIONS ........................................................................................... 12 ABSTRACT ................................................................................................................... 14 CHAPTER 1 INTRODUCTION .................................................................................................... 17 Uncertainty and Sensitivity Analysis ....................................................................... 17 Global Uncertainty and Sensitivity Analysis ............................................................ 18 Incorporating Spatial Heterogenity in Global Uncertainty and Sensitivity Analysis ......................................................................................................... 24 Research Objectives ............................................................................................... 26 2 EXPLORATORY GLOBAL UNCERTAINTY AND SENSITIVITY ANALYSIS, USING SPATIALLY LUMPED MODEL INPUTS ..................................................... 28 Introduction ............................................................................................................. 28 Test Case: Regional Simulation Model for Water Conservation Area-2A Application..................................................................................................... 28 Regional Simulation Model ........................................................................ 28 Model application to Water Conservation Area-2A .................................... 29 Model inputs and outputs ........................................................................... 31 Sensitivity and uncertainty methods previously applied to RSM ................ 33 Screening Method: Morris Elementary Effects ................................................. 35 Methodology ........................................................................................................... 38 Sensitivity Analysis Procedure ......................................................................... 38 Definition of Model Inputs and Outputs for the Screening SA........................... 39 Results .................................................................................................................... 40 Discussion .............................................................................................................. 41 Conclusions ............................................................................................................ 43 3 INCORPORATION OF SPATIAL UNCERTAINTY OF NUMERICAL MODEL INPUTS INTO GLOBAL UNCERTAINTY AND SENSITIVITY ANALYSIS OF A SPATIALLY DISTRIBUTED HYDROLOGICAL MODEL ......................................... 53 Introduction ............................................................................................................. 53 Incorporating Spatiality in Global Uncertainty and Sensitivity Analysis............. 53 5 Theory on Sequential Gaussian Simulation...................................................... 57 Theory on the Method of Sobol ........................................................................ 61 Methodology ........................................................................................................... 64 Land Elevation Data as an Example for Spatially Uncertain, Numerical Model Input ................................................................................................... 64 Implementation of Sequential Gaussian Simulation ......................................... 65 Linkage of SGS with the GUA/SA .................................................................... 68 Results .................................................................................................................... 71 Uncertainly Analysis Results ............................................................................ 71 Sensitivity Analysis Results .............................................................................. 73 Discussion .............................................................................................................. 74 Conclusions ............................................................................................................ 78 4 GLOBAL UNCERTAINTY AND SENSITIVITY ANALYSIS FOR SPATIALLY DISTRIBUTED HYDROLOGICAL MODELS, INCORPORATING SPATIAL UNCERTAINTY OF CATEGORICAL MODEL INPUTS. ......................................... 94 Introduction ............................................................................................................. 94 SIS of Categorical Variables ............................................................................. 95 WCA-2A Land Cover ........................................................................................ 97 Methodology ........................................................................................................... 98 Implementation of Sequential Indicator Simulation ........................................... 98 Associating RSM parameters with land use maps ......................................... 101 Implementation of the GUA/SA ...................................................................... 102 Results .................................................................................................................. 103 Uncertainty Analysis Results .......................................................................... 103 Sensitivity Analysis Results ............................................................................ 104 Discussion ............................................................................................................ 105 Conclusions .......................................................................................................... 108 5 UNCERTAINTY AND SENSITIVITY ANALYSIS AS A TOOL FOR OPTIMIZATION OF SPATIAL NUMERICAL DATA COLLECTION, USING LAND ELEVATION EXAMPLE. ............................................................................ 126 Introduction ........................................................................................................... 126 Spatial Input Data Resolution and Spatial Uncertainty ................................... 127 The Influence of Land Elevation Uncertainty on Hydrological Model Uncertainty .................................................................................................. 128 Propagation of DEM Uncertainty due to DEM Resolution .............................. 130 Methodology ......................................................................................................... 133 Description of Land Elevation Data Subsets .................................................. 133 Estimation of Spatial Uncertainty of Land Elevation ....................................... 134 Global Uncertainty and Sensitivity Analysis.................................................... 136 Results .................................................................................................................. 137 Sequential Gaussian Simulation Results ........................................................ 137 Global Uncertainty and Sensitivity Analysis Results ....................................... 138 Discussion ............................................................................................................ 140 6 Conclusions .......................................................................................................... 143 6 SUMMARY
Load more

Recommended publications
  • Sensitivity Analysis
    Chapter 11. Sensitivity Analysis Joseph A.C. Delaney, Ph.D. University of Washington, Seattle, WA John D. Seeger, Pharm.D., Dr.P.H. Harvard Medical School and Brigham and Women’s Hospital, Boston, MA Abstract This chapter provides an overview of study design and analytic assumptions made in observational comparative effectiveness research (CER), discusses assumptions that can be varied in a sensitivity analysis, and describes ways to implement a sensitivity analysis. All statistical models (and study results) are based on assumptions, and the validity of the inferences that can be drawn will often depend on the extent to which these assumptions are met. The recognized assumptions on which a study or model rests can be modified in order to assess the sensitivity, or consistency in terms of direction and magnitude, of an observed result to particular assumptions. In observational research, including much of comparative effectiveness research, the assumption that there are no unmeasured confounders is routinely made, and violation of this assumption may have the potential to invalidate an observed result. The analyst can also verify that study results are not particularly affected by reasonable variations in the definitions of the outcome/exposure. Even studies that are not sensitive to unmeasured confounding (such as randomized trials) may be sensitive to the proper specification of the statistical model. Analyses are available that 145 can be used to estimate a study result in the presence of an hypothesized unmeasured confounder, which then can be compared to the original analysis to provide quantitative assessment of the robustness (i.e., “how much does the estimate change if we posit the existence of a confounder?”) of the original analysis to violations of the assumption of no unmeasured confounders.
    [Show full text]
  • Design of Experiments for Sensitivity Analysis of a Hydrogen Supply Chain Design Model
    OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible This is an author’s version published in: http://oatao.univ-toulouse.fr/27431 Official URL: https://doi.org/10.1007/s41660-017-0025-y To cite this version: Ochoa Robles, Jesus and De León Almaraz, Sofia and Azzaro-Pantel, Catherine Design of Experiments for Sensitivity Analysis of a Hydrogen Supply Chain Design Model. (2018) Process Integration and Optimization for Sustainability, 2 (2). 95-116. ISSN 2509-4238 Any correspondence concerning this service should be sent to the repository administrator: [email protected] https://doi.org/10.1007/s41660-017-0025-y Design of Experiments for Sensitivity Analysis of a Hydrogen Supply Chain Design Model 1 1 1 J. Ochoa Robles • S. De-Le6n Almaraz • C. Azzaro-Pantel 8 Abstract Hydrogen is one of the most promising energy carriersin theq uest fora more sustainableenergy mix. In this paper, a model ofthe hydrogen supply chain (HSC) based on energy sources, production, storage, transportation, and market has been developed through a MILP formulation (Mixed Integer LinearP rograrnming). Previous studies have shown that the start-up of the HSC deployment may be strongly penaliz.ed froman economicpoint of view. The objective of this work is to perform a sensitivity analysis to identifythe major parameters (factors) and their interactionaff ectinga n economic criterion, i.e., the total daily cost (fDC) (response), encompassing capital and operational expenditures. An adapted methodology for this SA is the design of experiments through the Factorial Design and Response Surface methods.
    [Show full text]
  • A Selection Bias Approach to Sensitivity Analysis for Causal Effects*
    A Selection Bias Approach to Sensitivity Analysis for Causal Effects* Matthew Blackwell† April 17, 2013 Abstract e estimation of causal effects has a revered place in all elds of empirical political sci- ence, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are oen worried about the assumption of no omitted variables or no unmeasured confounders. is paper combines two approaches to sensitivity analysis to provide researchers with a tool to investigate how specic violations of no omitted variables alter their estimates. is approach can help researchers determine which narratives imply weaker results and which actually strengthen their claims. is gives researchers and critics a reasoned and quantitative approach to assessing the plausibility of causal effects. To demonstrate the ap- proach, I present applications to three causal inference estimation strategies: regression, matching, and weighting. *e methods used in this article are available as an open-source R package, causalsens, on the Com- prehensive R Archive Network (CRAN) and the author’s website. e replication archive for this article is available at the Political Analysis Dataverse as Blackwell (b). Many thanks to Steve Ansolabehere, Adam Glynn, Gary King, Jamie Robins, Maya Sen, and two anonymous reviewers for helpful comments and discussions. All remaining errors are my own. †Department of Political Science, University of Rochester. web: http://www.mattblackwell.org email: [email protected] Introduction Scientic progress marches to the drumbeat of criticism and skepticism. While the so- cial sciences marshal empirical evidence for interpretations and hypotheses about the world, an academic’s rst (healthy!) instinct is usually to counterattack with an alter- native account.
    [Show full text]
  • Sensitivity Analysis Methods for Identifying Influential Parameters in a Problem with a Large Number of Random Variables
    © 2002 WIT Press, Ashurst Lodge, Southampton, SO40 7AA, UK. All rights reserved. Web: www.witpress.com Email [email protected] Paper from: Risk Analysis III, CA Brebbia (Editor). ISBN 1-85312-915-1 Sensitivity analysis methods for identifying influential parameters in a problem with a large number of random variables S. Mohantyl& R, Code112 ‘Center for Nuclear Waste Regulato~ Analyses, SWH, Texas, USA 2U.S. Nuclear Regulatory Commission, Washington D. C., USA Abstract This paper compares the ranking of the ten most influential variables among a possible 330 variables for a model describing the performance of a repository for radioactive waste, using a variety of statistical and non-statistical sensitivity analysis methods. Results from the methods demonstrate substantial dissimilarities in the ranks of the most important variables. However, using a composite scoring system, several important variables appear to have been captured successfully. 1 Introduction Computer models increasingly are being used to simulate the behavior of complex systems, many of which are based on input variables with large uncertainties. Sensitivity analysis can be used to investigate the model response to these uncertain input variables. Such studies are particularly usefhl to identify the most influential variables affecting model output and to determine the variation in model output that can be explained by these variables. A sensitive variable is one that produces a relatively large change in model response for a unit change in its value. The goal of the sensitivity analyses presented in this paper is to find the variables to which model response shows the most sensitivity. There are a large variety of sensitivity analysis methods, each with its own strengths and weaknesses, and no method clearly stands out as the best.
    [Show full text]
  • Should Scenario Planning Abandon the Use of Narratives Describing
    Preparing for the future: development of an ‘antifragile’ methodology that complements scenario planning by omitting causation. Abstract This paper demonstrates that the Intuitive Logics method of scenario planning emphasises the causal unfolding of future events and that this emphasis limits its ability to aid preparation for the future, for example by giving a misleading impression as to the usefulness of ‘weak signals’ or ‘early warnings’. We argue for the benefits of an alternative method that views uncertainty as originating from indeterminism. We develop and illustrate an ‘antifragile’ approach to preparing for the future and present it as a step-by-step, non-deterministic methodology that can be used as a replacement for, or as a complement to, the causally-focused approach of scenario planning. Keywords: scenario planning; Intuitive Logics; causality; indeterminism; fragility; uncertainty 1. Introduction Scenario planning exercises are increasingly common in the private sector and are increasingly underpinned by academic research [1, 2 p.461, 3, 4 p.335]. While this increased popularity has been accompanied by a proliferation of approaches [5], it is widely accepted that most organizations employing scenario planning use an approach based on what is known as ‘Intuitive Logics’ (IL) [1, 6 p.9, 7 p.162]. Here, management team members are facilitated in thinking of the causal unfolding of chains of events into the future. 1 However, questions have been raised about IL’s effectiveness in preparing an organization for the future. In particular, much has recently been written highlighting its limited ability to deal with uncertainty - particularly its ability to assist in preparing for ‘surprise’ events [8-10].
    [Show full text]
  • Uncertainty and Sensitivity Analysis for Long-Running Computer Codes: a Critical Review
    UNCERTAINTY AND SENSITIVITY ANALYSIS FOR LONG-RUNNING COMPUTER CODES: A CRITICAL REVIEW By MASSACHUSE~s INSTrTUTE' Dustin R. Langewisch OF TECHNOLOGY B.S. Mechanical Engineering and B.S. Mathematics MAR 122010 University of Missouri-Columbia, 2007 L A [ LIBRARIES SUBMITTED TO THE DEPARTMENT OF NUCLEAR SCIENCE AND ENGINEERING IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF ARCHIVES MASTER OF SCIENCE IN NUCLEAR SCIENCE AND ENGINEERING AT THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY FEBRUARY 2010 The author hereby grants MIT permission to reproduce and distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created. Copyright 0 Massachusetts Institute of Technology All rights reserved. Signature of Author: "-1epartment of Nuclear Science and Engineering January 28, 2010 Certified by: Dr. Gofge E p-p'stolakis, Thesis Supervisor KEPCO Professor of Nuclear Science and Engineering Professor of Engineering Systems Certified by: Don d Helton, Thesis Reader of ear Regulatory Research a. Regulatory Commission Accepted by: Dr. Jacquelyn C. Yanch Chair, Department Committee on Graduate Students UNCERTAINTY AND SENSITIVITY ANALYSIS FOR LONG-RUNNING COMPUTER CODES: A CRITICAL REVIEW Dustin R. Langewisch SUBMITTED TO THE DEPARTMENT OF NUCLEAR SCIENCE AND ENGINEERING IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN NUCLEAR SCIENCE AND ENGINEERING AT THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY FEBRUARY 2010 Abstract This thesis presents a critical review of existing methods for performing probabilistic uncertainty and sensitivity analysis for complex, computationally expensive simulation models. Uncertainty analysis (UA) methods reviewed include standard Monte Carlo simulation, Latin Hypercube sampling, importance sampling, line sampling, and subset simulation.
    [Show full text]
  • Sensitivity Analysis
    Where to find this talk: www.andreasaltelli.eu On sensitivity analysis and its take up Definitions Uncertainty analysis: Focuses on quantifying the uncertainty in model output Sensitivity analysis: The study of the relative importance of different input factors on the model output Dr. Qiongli Wu, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, China European Commission, 2015 Office for the Management and Budget, 2006 Environmental Protection Agency, 2009 EPA, 2009, March. Guidance on the Development, Evaluation, and Application of Environmental Models. Technical Report EPA/100/K-09/003. Office of the Science Advisor, Council for Regulatory Environmental Modeling, http://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P1003E4R.PDF, Last accessed December 2015. EUROPEAN COMMISSION, Better regulation toolbox, appendix to the Better Regulation Guidelines, Strasbourg, 19.5.2015, SWD(2015) 111 final, COM(2015) 215 final, http://ec.europa.eu/smart- regulation/guidelines/docs/swd_br_guidelines_en.pdf. OMB, Proposed risk assessment bulletin, Technical report, The Office of Management and Budget’s – Office of Information and Regulatory Affairs (OIRA), January 2006, https://www.whitehouse.gov/sites/default/files/omb/assets/omb/inforeg/proposed_risk_assessment_bulleti n_010906.pdf, pp. 16–17, accessed December 2015. http://ec.europa. eu/smart- regulation/ Source: IA Toolbox, p. 391 An engineer’s vision of UA, SA Resolution levels model structures errors Simulation uncertainty analysis Model model data output sensitivity analysis
    [Show full text]
  • A Receiver Operating Characteristic Framework for Non-Adherence
    The AAPS Journal (2019) 21: 40 DOI: 10.1208/s12248-019-0299-9 Research Article A Receiver Operating Characteristic Framework for Non-adherence Detection Using Drug Concentration Thresholds—Application to Simulated Risperidone Data in Schizophrenic Patients Carlos Pérez-Ruixo,1,3 Bart Remmerie,1 Juan José Peréz-Ruixo,1 and An Vermeulen2 Received 19 September 2018; accepted 12 January 2019; published online 14 March 2019 Abstract. Non-adherence to antipsychotic medication is a primary factor in disease relapse in schizophrenic patients. We sought to evaluate if plasma concentrations of the antipsychotic risperidone can be used as a predictor of treatment adherence and to identify the optimal plasma concentration threshold to reliably distinguish between adherent and non-adherent patients. A population pharmacokinetic model was used to simulate plasma risperidone steady-state trough concentrations in 1000 virtual patients, where 60% of the patients were 100% adherent to their medication, while 40% of the patients were non- adherent to their medication. The probability of adherence was assessed by receiver operating characteristic (ROC) analysis on Ctrough. The area under the ROC curve (AUCROC) was used to identify the optimal Ctrough threshold. Single vs multiple Ctrough at steady state was also evaluated. After a single risperidone Ctrough measurement, the AUCROC (95% CI) was estimated to be 0.71 (0.69–0.72) and the optimal Ctrough threshold accounting for the lowest number of adherent and non-adherent misclassifications was estimated to be 11.9 ng/mL. After multiple Ctrough measurements, the AUCROC (95% CI) increased up to 0.85 (0.84–0.87) for three Ctrough measurements.
    [Show full text]
  • Shapley Effects for Sensitivity Analysis with Dependent Inputs
    Shapley effects for sensitivity analysis with dependent inputs: comparisons with Sobol’ indices, numerical estimation and applications Bertrand Iooss, Clémentine Prieur To cite this version: Bertrand Iooss, Clémentine Prieur. Shapley effects for sensitivity analysis with dependent inputs: comparisons with Sobol’ indices, numerical estimation and applications. 2018. hal-01556303v3 HAL Id: hal-01556303 https://hal.inria.fr/hal-01556303v3 Preprint submitted on 22 Feb 2018 (v3), last revised 23 Nov 2019 (v7) HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Shapley effects for sensitivity analysis with dependent inputs: comparisons with Sobol' indices, numerical estimation and applications Bertrand Iooss EDF Lab Chatou, 6 Quai Watier, 78401 Chatou, France Institut de Math´ematiquesde Toulouse, 31062, Toulouse, France Cl´ementine Prieur Universit´eGrenoble Alpes, CNRS, LJK, F-38000 Grenoble, France Inria project/team AIRSEA Abstract The global sensitivity analysis of a numerical model aims to quantify, by means of sensitivity indices estimate, the contributions of each uncertain input variable to the model output uncertainty. The so-called Sobol' indices, which are based on the functional variance analysis, present a difficult interpretation in the pres- ence of statistical dependence between inputs.
    [Show full text]
  • (Covid-19) in Predicting Future Behaviours and Sensitivity Analysis
    Math. Model. Nat. Phenom. 15 (2020) 33 Mathematical Modelling of Natural Phenomena https://doi.org/10.1051/mmnp/2020020 www.mmnp-journal.org MATHEMATICAL MODELLING FOR CORONAVIRUS DISEASE (COVID-19) IN PREDICTING FUTURE BEHAVIOURS AND SENSITIVITY ANALYSIS Sarbaz H. A. Khoshnaw1,*, Rizgar H. Salih1 and Sadegh Sulaimany2 Abstract. Nowadays, there are a variety of descriptive studies of available clinical data for coron- avirus disease (COVID-19). Mathematical modelling and computational simulations are effective tools that help global efforts to estimate key transmission parameters. The model equations often require computational tools and dynamical analysis that play an important role in controlling the disease. This work reviews some models for coronavirus first, that can address important questions about the global health care and suggest important notes. Then, we model the disease as a system of differential equations. We develop previous models for the coronavirus, some key computational simulations and sensitivity analysis are added. Accordingly, the local sensitivities for each model state with respect to the model parameters are computed using three different techniques: non-normalizations, half nor- malizations and full normalizations. Results based on sensitivity analysis show that almost all model parameters may have role on spreading this virus among susceptible, exposed and quarantined sus- ceptible people. More specifically, communicate rate person{to{person, quarantined exposed rate and transition rate of exposed individuals have an effective role in spreading this disease. One possible solution suggests that healthcare programs should pay more attention to intervention strategies, and people need to self-quarantine that can effectively reduce the disease. Mathematics Subject Classification. 92D30,92D25,92C42, 34C60.
    [Show full text]
  • A Survey of Sensitivity Analysis Methodology
    u S NBS Publi cations NBSIR 84-2814 NAT'L INST. OF STAND & TECH AlllOb E b 1 7 fi V Survey of Sensitivity m Analysis Methodology U S. DEPARTMENT OF COMMERCE National Bureau of Standards Center For Applied Mathematics Operations Research Division Washington, DC 20234 February 1 984 Sponsored by: U S. DEPARTMENT OF COMMERCE National Bureau of Standards National Engineering Laboratory * Center for Applied Mathematics -QC Washington, DC 20234 100 ,U56 84-2314 1384 NBSIR 84-2814 A SURVEY OF SENSITIVITY ANALYSIS METHODOLOGY Robert G. Hendrickson U.S. DEPARTMENT OF COMMERCE National Bureau of Standards Center for Applied Mathematics Operations Research Division Washington, DC 20234 February 1 984 Sponsored by: U.S. DEPARTMENT OF COMMERCE National Bureau of Standards National Engineering Laboratory Center for Applied Mathematics Washington, DC 20234 U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director Table of Contents Page 1.0 INTRODUCTION 1 . 1 Background 1 1.2 Motivation and Objectives 2 1.3 Organization 4 2.0 ERROR TYPES AND MODEL STRUCTURES 2.1 Introduction 5 2.2 Error Types 5 2.2.1 Accuracy of Numerical Values 5 2.2.2 Process Errors 6 2.2.3 System Errors 7 2.2.4 Computational Errors 8 2.2.5 Errors of Sensitivity Analysis 8 2.3 Function Types 8 2.4 Model Types 10 3.0 CLASSICAL METHODS OF SENSITIVITY ANALYSIS 3.1 Introduction 12 3.2 Precision In Computation 12 3.2.1 Numerical Significance 12 3.2.2 Rounding Errors 13 3.3 General Formulations of Error Analysis 14 3.3.1 Theory of Errors 14 3.
    [Show full text]
  • What Is an Optimal Value of K in K-Fold Cross-Validation in Discrete Bayesian Network Analysis?
    What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? Bruce G. Marcot & Anca M. Hanea Computational Statistics ISSN 0943-4062 Comput Stat DOI 10.1007/s00180-020-00999-9 1 23 Your article is protected by copyright and all rights are held exclusively by This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply. This e-offprint is for personal use only and shall not be self- archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”. 1 23 Author's personal copy Computational Statistics https://doi.org/10.1007/s00180-020-00999-9 ORIGINAL PAPER What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? Bruce G. Marcot1 · Anca M. Hanea2 Received: 8 November 2019 / Accepted: 4 June 2020 © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020 Abstract Cross-validation using randomized subsets of data—known as k-fold cross-valida- tion—is a powerful means of testing the success rate of models used for classifca- tion.
    [Show full text]