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Nicky Best
September 2010
IMS Bulletin 39(4)
Tea: a High-Level Language and Runtime System for Automating
December 2000
The BUGS Project: Evolution, Critique and Future Directions
Design and Analysis Issues in Family-Based Association
Stan User's Guide 2.27
A Joint Spatio-Temporal Model of Opioid Associated Deaths and Treatment Admissions in Ohio
Harrison Zhou
Invited Conference Speakers
Meta-Analysis of Diagnostic Test Data: Modern Statistical Approaches
IMS Bulletin 2
An Improved R-Hat for Assessing Convergence of MCMC
December 2003
IMS Bulletin, March 2010
Combining Robustness and Efficiency in Gradient-Based MCMC
RSS International Conference for All Statisticians and Users of Data
Tea: a High-Level Language and Runtime System for Automating Statistical Analyses
Top View
Looking Back Over (Nearly) 75 Years
Bayesian Hierarchical Models for Meta-Analysis
Amstatnews the Membership Magazine of the American Statistical Association •
Spatiotemporal Bayesian Hierarchical Models, with Application to Birth Outcomes Jonathan D
Hamiltonian Monte Carlo with Energy Conserving Subsampling
NCRM Collaborative Fund Project: Adapting Econometric Causal Effect Estimators to the Pub- Lic Health Arena Final Report
ENAR Would Like to Acknowledge the Generous Support of the 2010 Local
7.1 BYM Model: Rubin and Gelman Convergence Diagnostics
A Complete Bibliography of the Journal of the American Statistical Association: 2010–2019
Strategy for Modelling Nonrandom Missing Data Mechanisms in Observational Studies Using Bayesian Methods
Bayesian Demography 250 Years After Bayes
Curriculum Vitae Andrew Gelman
ALEXANDER PHILIP DAWID Emeritus Professor of Statistics University of Cambridge E-Mail:
[email protected]
Web
Bayesian Nonparametric Models for Spatially Indexed Data of Mixed Type
A Complete Bibliography of the Journal of the Royal Statistical Society, Series a Family: 1990–1999
Looking Ahead to Gothenburg
Composable Probabilistic Inference with Blaise Keith a Bonawitz
Amstatnews Advertising Directory
Sujit K. Ghosh
Texts in Statistical Science Bayesian Statistical Methods Have Become Widely Used for Data
Firefly Monte Carlo: Exact MCMC with Subsets of Data
A Second-Order Gradient Method for Speeding up MCMC
Machine Learning in Space and Time Spatiotemporal Learning and Inference with Gaussian Processes and Kernel Methods
September 2000
Introduction to Bayesian Inference in Biomedical Applications
Modern Monte Carlo Methods and Their Application In
A. P. DAWID Publications