Metropolis-Hastings algorithms with adaptive proposals

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dc.contributor.author Cai, B en
dc.contributor.author Meyer, Renate en
dc.contributor.author Perron, F en
dc.date.accessioned 2012-03-07T20:10:37Z en
dc.date.issued 2008 en
dc.identifier.citation Statistics and Computing 18(4):421-433 2008 en
dc.identifier.issn 0960-3174 en
dc.identifier.uri http://hdl.handle.net/2292/13291 en
dc.description.abstract Different strategies have been proposed to improve mixing and convergence properties of Markov Chain Monte Carlo algorithms. These are mainly concerned with customizing the proposal density in the Metropolis–Hastings algorithm to the specific target density and require a detailed exploratory analysis of the stationary distribution and/or some preliminary experiments to determine an efficient proposal. Various Metropolis–Hastings algorithms have been suggested that make use of previously sampled states in defining an adaptive proposal density. Here we propose a general class of adaptive Metropolis–Hastings algorithms based on Metropolis–Hastings-within-Gibbs sampling. For the case of a one-dimensional target distribution, we present two novel algorithms using mixtures of triangular and trapezoidal densities. These can also be seen as improved versions of the all-purpose adaptive rejection Metropolis sampling (ARMS) algorithm to sample from non-logconcave univariate densities. Using various different examples, we demonstrate their properties and efficiencies and point out their advantages over ARMS and other adaptive alternatives such as the Normal Kernel Coupler. en
dc.publisher Springer en
dc.relation.ispartofseries Statistics and Computing en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title Metropolis-Hastings algorithms with adaptive proposals en
dc.type Journal Article en
dc.identifier.doi 10.1007/s11222-008-9051-5 en
pubs.begin-page 421 en
pubs.volume 18 en
dc.rights.holder Copyright: Springer en
pubs.end-page 433 en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Article en
pubs.elements-id 91957 en
pubs.org-id Science en
pubs.org-id Statistics en
pubs.record-created-at-source-date 2010-09-01 en


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