Bayesian Modelling, Inference, Prediction and Decision-Making
Bayesian methods offer an approach to inference, prediction and decision-making that allow you to synthesize all relevant sources of information in drawing conclusions and making decisions in the presence of uncertainty: you can bring together information both internal to, and external to, your data set to create a logically consistent summary of all that's known about the problem you're studying.
This course will:
Who Should Attend?
Statisticians, biostatisticians, epidemiologists, data analysts, data-miners, machine-learning specialists and data scientists who wish to broaden and deepen:
However, all necessary concepts will be approached in a sufficiently intuitive manner that rustiness on these topics will not prevent understanding of the key ideas.
How You Will Benefit
What Do We Cover?
Practical work will be done in: A mixture of R and WinBUGS
Note: For practical work, participants must bring their own laptop with fully licensed versions of the chosen software.
Related Courses: This is part of a series of Bayesian Modelling courses, presented by Professor David Draper:
Please note that there will not be any printed notes for this course. Materials for 2017 can be seen on the following links:
Guest Presenter (last updated September 2017): David Draper
David Draper is a Professor of Statistics in the Department of Applied Mathematics and Statistics at the University of California, Santa Cruz (USA); he has also been a Senior Principal Research Scientist at both eBay Research Labs and at Amazon Research, where he developed new methods for Bayesian analysis of very large data sets in the discipline of data science, and he is currently in a consultative role as Senior Analyst at the social finance company SoFi.
He is a Fellow of the American Association for the Advancement of Science, the American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS), and the Royal Statistical Society (RSS); 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 150 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 more than 1,600 times, and taken together his publications have been cited about 14,000 times.
His research is in the areas of Bayesian inference and prediction, model uncertainty and empirical model-building, hierarchical Modelling, Markov Chain Monte Carlo methods, Bayesian nonparametric methods and data science, with applications mainly in medicine, health policy, education, environmental risk assessment and eCommerce.
His short courses have received Excellence in Continuing Education Awards from the American Statistical Association on two occasions, corresponding to days 1 and 2 of this week of courses (20-24 November 2017). 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.