Data Analysis Papers
Bayesian Modeling Using the MCMC Procedure
Chen, Fang; SAS Institute 2009
Abstract
Bayesian methods have become increasingly popular in modern statistical analysis and are being applied to a broad spectrum of scientific fields and research areas. This paper introduces the new MCMC procedure in SAS/STAT 9.2, which is designed for general-purpose Bayesian computations. The MCMC procedure enables you to carry out analysis on a wide range of complex Bayesian statistical models. The procedure uses the Markov chain Monte Carlo (MCMC) algorithm to draw samples from an arbitrary posterior distribution, which is defined by the prior distributions for the parameters and the likelihood function for the data that you specify. This paper describes how to use the MCMC procedure for estimation, inference, and prediction.
Statistics and Operations
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