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Interacting multiple try algorithms with different proposal distributions

Casarin, Roberto, Craiu, Radu, Leisen, Fabrizio (2013) Interacting multiple try algorithms with different proposal distributions. Statistics and Computing, 23 (2). pp. 185-200. ISSN 0960-3174. (doi:10.1007/s11222-011-9301-9) (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)

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. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/s11222-011-9301-9

Abstract

We introduce a new class of interacting Markov chain Monte Carlo (MCMC) algorithms which is designed to increase the efficiency of a modified multiple-try Metropolis (MTM) sampler. The extension with respect to the existing MCMC literature is twofold. First, the sampler proposed extends the basic MTM algorithm by allowing for different proposal distributions in the multiple-try generation step. Second, we exploit the different proposal distributions to naturally introduce an interacting MTM mechanism (IMTM) that expands the class of population Monte Carlo methods and builds connections with the rapidly expanding world of adaptive MCMC. We show the validity of the algorithm and discuss the choice of the selection weights and of the different proposals. The numerical studies show that the interaction mechanism allows the IMTM to efficiently explore the state space leading to higher efficiency than other competing algorithms.

Item Type: Article
DOI/Identification number: 10.1007/s11222-011-9301-9
Subjects: H Social Sciences > HA Statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Fabrizio Leisen
Date Deposited: 07 Jun 2014 09:44 UTC
Last Modified: 01 Aug 2019 10:36 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/36522 (The current URI for this page, for reference purposes)
Leisen, Fabrizio: https://orcid.org/0000-0002-2460-6176
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