AP Statistics Curriculum 2007 Bayesian Gibbs

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Revision as of 06:02, 2 June 2009

Gibbs sampling is an algorithm to generate a sequence of samples from the joint probability distribution of two or more random variables. The purpose of this sequence is to approximate the joint distribution, or to compute an expected value. Gibbs sampling is a special case of the Metropolis-Hastings algorithm also making it an example of a Markov chain Monte Carlo algorithm.

Contents 1 Introduction to numerical methods 2 EM algorithm 3 Data augmentation by Monte Carlo 4 The Gibbs Sampler 5 Rejection Sampling 6 Metropolis Hastings Algorithm 7 Generalized Linear Model

Introduction to numerical methods

EM algorithm

Data augmentation by Monte Carlo

The Gibbs Sampler

Rejection Sampling

Metropolis Hastings Algorithm

Generalized Linear Model