Department of
Applied Mathematics & Statistics
(AMS)
Reading 2017, Day 4: Bayesian Model Specification
Topics will include the big picture in Bayesian model specification; five foundational principles for specifying Bayesian models: the Calibration Principle, the Modeling-As-Decision Principle, the Prediction Principle, the Inference-Versus-Decision Principle and parts 1 and 2 of Cromwell's Rule; model expansion as a tool for improving Bayesian models: embedding a deficient model M in a larger class of models, of which M is a special case; methods for finding good Bayesian models: Calibration Cross-Validation, Bayes factors, BIC, DIC and log scores; a generic Bayesian model-search algorithm; and false-positive/false-negative tradeoffs in comparing {Bayes factors, BIC} and {DIC, log scores} on their ability to correctly discriminate between models
The case studies will be drawn from gerontology (analysis of a randomized controlled experiment to measure the effect of a health intervention on hospitalization rates for elderly non-institutionalized people), health policy (assessing hospital quality of care by monitoring the rate of unplanned transfer to the Intensive Care Unit); measurement of physical constants, and medicine (randomized controlled trials of drugs to lower blood pressure in hypertensive patients, to (a) estimate improvement of one drug over another and (b) establish bio-equivalence of two drugs).
Through a series of practicals (computer labs), the course will liberally illustrate user-friendly implementations of MCMC sampling via the freeware programs WinBUGS (for Windows platforms) and rjags (for all platforms) when closed-form solutions are not possible.
The course is intended mainly for people who often use statistics in their research or other work in academia, government or industry; as noted above, a first and second course in Bayesian analysis equivalent to the content of day 3 of this five-day course will provide sufficient background for participants.
He is a Fellow of the American Association for the Advancement of Science, the American Statistical Association (ASA), the Institute of Mathematical Statistics, and the Royal Statistical Society; from 2001 to 2003 he served as the President-Elect, President, and Past President of the International Society for Bayesian Analysis (ISBA).
He is the author or co-author of about 145 contributions to the methodological and applied statistical literature, including articles in the Journal of the Royal Statistical Society (Series A, B and C), the Journal of the American Statistical Association, the Annals of Applied Statistics, Bayesian Analysis, Statistical Science, the New England Journal of Medicine, and the Journal of the American Medical Association; his 1995 JRSS-B article on assessment and propagation of model uncertainty has been cited about 1,650 times, and taken together his publications have been cited about 14,500 times.
His research is in the areas of Bayesian inference and prediction, model uncertainty and empirical model-building, hierarchical modeling, Markov Chain Monte Carlo methods, and Bayesian nonparametric methods, with applications mainly in medicine, health policy, education, environmental risk assessment and data science.
His short courses have received Excellence in Continuing Education Awards from the American Statistical Association on two occasions. He has won or been nominated for major teaching awards everywhere he has taught (the University of Chicago; the RAND Graduate School of Public Policy Studies; the University of California, Los Angeles; the University of Bath (UK); and the University of California, Santa Cruz).
He has a particular interest in the exposition of complex statistical methods and ideas in the context of real-world applications.
(last modified: 18 November 2017)