Understanding the Metropolis-Hastings Algorithm

@article{citeulike:541115, abstract = {We provide a detailed, introductory exposition of the Metropolis-Hastings algorithm, a powerful Markov chain method to simulate multivariate distributions. A simple, intuitive derivation of this method is given along with guidance on implementation. Also discussed are two applications of the algorithm, one for implementing acceptance-rejection sampling when a blanketing function is not available and the other for implementingthe algorithm with block-at-a-time scans. In the latter situation many different algorithms, including the Gibbs sampler are shown to be special cases of the Metropolis-Hastings algorithm. The methods are ilustrated with examples.}, author = {Chib}, citeulike-article-id = {541115}, journal = {American Statistician}, posted-at = {2006-03-09 01:58:00}, priority = {2}, title = {Understanding the Metropolis-Hastings Algorithm}, year = {1989} }

TY - JOUR ID - citeulike:541115 L3 - citeulike-article-id:541115 TI - Understanding the Metropolis-Hastings Algorithm JF - American Statistician N2 - We provide a detailed, introductory exposition of the Metropolis-Hastings algorithm, a powerful Markov chain method to simulate multivariate distributions. A simple, intuitive derivation of this method is given along with guidance on implementation. Also discussed are two applications of the algorithm, one for implementing acceptance-rejection sampling when a blanketing function is not available and the other for implementingthe algorithm with block-at-a-time scans. In the latter situation many different algorithms, including the Gibbs sampler are shown to be special cases of the Metropolis-Hastings algorithm. The methods are ilustrated with examples. AU - Chib PY - 1989/// ER -