Open Access
2010 Use in practice of importance sampling for repeated MCMC for Poisson models
Dorota Gajda, Chantal Guihenneuc-Jouyaux, Judith Rousseau, Kerry Mengersen, Darfiana Nur
Electron. J. Statist. 4: 361-383 (2010). DOI: 10.1214/09-EJS527


The Importance Sampling method is used as an alternative approach to MCMC in repeated Bayesian estimations. In the particular context of numerous data sets, MCMC algorithms have to be called on several times which may become computationally expensive. Since Importance Sampling requires a sample from a posterior distribution, our idea is to use MCMC to generate only a certain number of Markov chains and use them later in the subsequent IS estimations. For each Importance Sampling procedure, the suitable chain is selected by one of three criteria we present here. The first and second criteria are based on the L1 norm of the difference between two posterior distributions and their Kullback-Leibler divergence respectively. The third criterion results from minimizing the variance of IS estimate. A supplementary automatic selection procedure is also proposed to choose those posterior for which Markov chains will be generated and to avoid arbitrary choice of importance functions. The featured methods are illustrated in simulation studies on three types of Poisson model: simple Poisson model, Poisson regression model and Poisson regression model with extra Poisson variability. Different parameter settings are considered.


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Dorota Gajda. Chantal Guihenneuc-Jouyaux. Judith Rousseau. Kerry Mengersen. Darfiana Nur. "Use in practice of importance sampling for repeated MCMC for Poisson models." Electron. J. Statist. 4 361 - 383, 2010.


Published: 2010
First available in Project Euclid: 17 March 2010

zbMATH: 1329.62129
MathSciNet: MR2645489
Digital Object Identifier: 10.1214/09-EJS527

Primary: 62F15 , 65C05
Secondary: 65C60

Keywords: importance sampling , MCMC , Poisson model

Rights: Copyright © 2010 The Institute of Mathematical Statistics and the Bernoulli Society

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