%0 Journal Article %1 chib1995understanding %A Chib, Siddhartha %A Greenberg, Edward %D 1995 %J The American Statistician %K approximation bayes chib estimation hastings hm metropolis mixedtrails mtmc posterior %N 4 %P 327-335 %R 10.1080/00031305.1995.10476177 %T Understanding the Metropolis-Hastings Algorithm %U http://amstat.tandfonline.com/doi/abs/10.1080/00031305.1995.10476177 %V 49 %X 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 implementing the 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 illustrated with examples.

@article{chib1995understanding, abstract = { 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 implementing the 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 illustrated with examples. }, added-at = {2016-09-16T08:10:12.000+0200}, author = {Chib, Siddhartha and Greenberg, Edward}, biburl = {https://www.bibsonomy.org/bibtex/2eca2e182789da65a9bccebd62951745e/becker}, doi = {10.1080/00031305.1995.10476177}, eprint = {http://amstat.tandfonline.com/doi/pdf/10.1080/00031305.1995.10476177}, interhash = {ec7af487b3b0d0a525972cb7a8e48647}, intrahash = {eca2e182789da65a9bccebd62951745e}, journal = {The American Statistician}, keywords = {approximation bayes chib estimation hastings hm metropolis mixedtrails mtmc posterior}, number = 4, pages = {327-335}, timestamp = {2017-04-05T11:47:46.000+0200}, title = {Understanding the Metropolis-Hastings Algorithm}, url = {http://amstat.tandfonline.com/doi/abs/10.1080/00031305.1995.10476177}, volume = 49, year = 1995 }