Adaptive proposal distribution for random walk Metropolis algorithm

SummaryThe choice of a suitable MCMC method and further the choice of a proposal distribution is known to be crucial for the convergence of the Markov chain. However, in many cases the choice of an effective proposal distribution is difficult. As a remedy we suggest a method called Adaptive Proposal...

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Bibliographic Details
Published inComputational statistics Vol. 14; no. 3; pp. 375 - 395
Main Authors Haario, Heikki, Saksman, Eero, Tamminen, Johanna
Format Journal Article
LanguageEnglish
Published Heidelberg Springer Nature B.V 01.09.1999
Subjects
Adaptive algorithms
Algorithms
Markov analysis
Markov chains
Parameter estimation
Random walk
Satellite instruments
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Summary:SummaryThe choice of a suitable MCMC method and further the choice of a proposal distribution is known to be crucial for the convergence of the Markov chain. However, in many cases the choice of an effective proposal distribution is difficult. As a remedy we suggest a method called Adaptive Proposal (AP). Although the stationary distribution of the AP algorithm is slightly biased, it appears to provide an efficient tool for, e.g., reasonably low dimensional problems, as typically encountered in non-linear regression problems in natural sciences. As a realistic example we include a successful application of the AP algorithm in parameter estimation for the satellite instrument ‘GOMOS’. In this paper we also present systematic performance criteria for comparing Adaptive Proposal algorithm with more traditional Metropolis algorithms.
ISSN:0943-4062
1613-9658
DOI:10.1007/s001800050022