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Fang Chen

2014-12-22
09:30:00 - 11:30:00

103 , Mathematics Research Center Building (ori. New Math. Bldg.)

Bayesian methods are becoming ever more popular and important in applied and fundamental research. In this tutorial, I provide a gentle introduction to the Bayesian paradigm and related concepts. The objectives are to familiarize students with the essentials of Bayesian analysis and computing.

First I review differences between classical and Bayesian approaches to inferences and concepts in estimation. Thereafter, the advantages of Bayesian analysis are discussed and the fundamentals of prior distributions are presented. The tutorial then covers MCMC methods and related simulation techniques, emphasizing the interpretation of convergence diagnostics in practice.

This tutorial is the first part of a two-part series, with the second tutorial focuses more on the computational aspect of Bayesian statistics (Practical Computation using PROC MCMC). Bayesian treatment of a wide range of statistical models using software will be explored further in the second tutorial.