Metropolis Sampling

Luca Martino, Victor Elvira

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Monte Carlo (MC) sampling methods are widely applied in Bayesian inference, system simulation, and optimization problems. The Markov chain Monte Carlo (MCMC) algorithms are a well‐known class of MC methods that generate a Markov chain with the desired invariant distribution. In this document, we focus on the Metropolis–Hastings (MH) sampler, which can be considered as the atom of the MCMC techniques, introducing the basic notions and different properties. We describe in detail all the elements involved in the MH algorithm and the most relevant variants. Several improvements and recent extensions proposed in the literature are also briefly discussed, providing a quick but exhaustive overview of the current Metropolis‐based sampling's world.
Original languageEnglish
Title of host publicationWiley StatsRef: Statistics Reference Online
EditorsN. Balakrishnan, Theodore Colton, Brian Everitt, Walter Piegorsch, Fabrizio Ruggeri, Jozef L. Teugels
PublisherJohn Wiley & Sons Inc.
Pages1-18
ISBN (Electronic)9781118445112
DOIs
Publication statusPublished - 15 May 2017

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