On adaptive Metropolis-Hastings method

Griffin, Jim E. and Walker, Stephen G. (2013) On adaptive Metropolis-Hastings method. Statistics and Computing, 23 (1). pp. 123-134. ISSN 0960-3174. (doi:https://doi.org/10.1007/s11222-011-9296-2) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Abstract

This paper presents a method for adaptation in Metropolis–Hastings algorithms. A product of a proposal density and K copies of the target density is used to define a joint density which is sampled by a Gibbs sampler including a Metropolis step. This provides a framework for adaptation since the current value of all K copies of the target distribution can be used in the proposal distribution. The methodology is justified by standard Gibbs sampling theory and generalizes several previously proposed algorithms. It is particularly suited to Metropolis-within-Gibbs updating and we discuss the application of our methods in this context. The method is illustrated with both a Metropolis–Hastings independence sampler and a Metropolis-with-Gibbs independence sampler. Comparisons are made with standard adaptive Metropolis–Hastings methods.

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 29 May 2014 15:19 UTC
Last Modified: 29 May 2014 15:19 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41217 (The current URI for this page, for reference purposes)
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