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Article Dans Une Revue Journal of Computational and Graphical Statistics Année : 2011

Using parallel computation to improve Independent Metropolis--Hastings based estimation

Résumé

In this paper, we consider the implications of the fact that parallel raw-power can be exploited by a generic Metropolis--Hastings algorithm if the proposed values are independent. In particular, we present improvements to the independent Metropolis--Hastings algorithm that significantly decrease the variance of any estimator derived from the MCMC output, for a null computing cost since those improvements are based on a fixed number of target density evaluations. Furthermore, the techniques developed in this paper do not jeopardize the Markovian convergence properties of the algorithm, since they are based on the Rao--Blackwell principles of Gelfand and Smith (1990), already exploited in Casella and Robert (1996), Atchade and Perron (2005) and Douc and Robert (2010). We illustrate those improvement both on a toy normal example and on a classical probit regression model but insist on the fact that they are universally applicable.

Dates et versions

hal-00567242 , version 1 (19-02-2011)

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Pierre Jacob, Christian Robert, Murray H. Smith. Using parallel computation to improve Independent Metropolis--Hastings based estimation. Journal of Computational and Graphical Statistics, 2011, 20 (3), pp.1-19. ⟨10.1198/jcgs.2011.10167⟩. ⟨hal-00567242⟩
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