Estimation via Markov chain Monte Carlo

Author

Spall, James C.

Author_Institution

Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA

Volume

23

Issue

2

fYear

2003

fDate

4/1/2003 12:00:00 AM

Firstpage

34

Lastpage

45

Abstract

Markov chain Monte Carlo (MCMC) is a powerful means for generating random samples that can be used in computing statistical estimates and marginal and conditional probabilities. MCMC methods rely on dependent (Markov) sequences having a limiting distribution corresponding to a distribution of interest. This article is a survey of popular implementations of MCMC, focusing particularly on the two most popular specific implementations of MCMC: Metropolis-Hastings (M-H) and Gibbs sampling. Our aim is to provide the reader with some of the central motivation and the rudiments needed for a straightforward application.

Keywords

Markov processes; Monte Carlo methods; probability; sampling methods; Gibbs sampling; Markov chain Monte Carlo; Metropolis-Hastings algorithm; conditional probabilities; limiting distribution; marginal probabilities; random samples; statistical estimation; Bayesian methods; Books; Monte Carlo methods; Physics; Power generation; Probability; Sampling methods; Statistics; Stochastic processes; Terminology;

fLanguage

English

Journal_Title

Control Systems, IEEE

Publisher

ieee

ISSN

1066-033X

Type

jour

DOI

10.1109/MCS.2003.1188770

Filename

1188770