JSM 2011 Online Program

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Activity Details


CE_07C Sun, 7/31/2011, 8:30 AM - 5:00 PM HQ-Americana Salon 2
Bayesian Inference — Continuing Education Course
ASA , Section on Bayesian Statistical Science
Instructor(s): Bruno Sanso, University of California at Santa Cruz
Bayesian methods have become increasingly popular with the advent of fast computational algorithms for the exploration of high dimensional probability distributions. The Bayesian paradigm provides a coherent framework to build models of high complexity, incorporate quantitative and structural prior information and account for all uncertainties in a probabilistic way. This course reviews the bases of Bayesian inference. The course will start by presenting the basic elements of statistical inference that uses likelihood functions. We will then consider the problem of specifying prior distributions, proceed by describing the tools for both pointwise and interval estimation and prediction and present the Bayesian theory of hypothesis testing and model comparison. Finally we will review the elements of modern computational methods used in the applications of Bayesian models. To illustrated the methods, the course draws examples from a variety of fields where the author has successfully applied Bayesian methods. But the emphasis of the course is on the general concepts that support the Bayesian paradigm. The course will not consider specific modeling techniques in depth. The course targets students or professionals with a good knowledge of statistics that want to learn or refresh their knowledge of basic Bayesian inference. The level of mathematical sophistication will be kept as low as possible. Calculus and basic probability theory are considered a pre-requisite.



2011 JSM Online Program Home

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