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A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
Activity Details
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| CE_23C | Tue, 8/5/2014, 8:30 AM - 5:00 PM | CC-161 | |
| Bayesian Inference — Professional Development Continuing Education Course | |||
| ASA , Section on Bayesian Statistical Science | |||
| 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. As such, it focuses on key general concepts rather than the technical detail of specific methods. 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. 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. | |||
| Instructor(s): Bruno Sanso, University of California, Santa Cruz | |||
2014 JSM Online Program Home
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