Parallel hierarchical sampling:a general-purpose class of multiple-chains MCMC algorithms

Antonietta Mira () and Fabio Rigat ()
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Antonietta Mira: Department of Economics, University of Insubria, Italy
Fabio Rigat: Department of Statistics and Centre for analytical Science, University of Warwick, UK

Economics and Quantitative Methods from Department of Economics, University of Insubria

Abstract: This paper introduces the Parallel Hierarchical Sampler (PHS), a class of Markov chain Monte Carlo algorithms using several interacting chains having the same target distribution but different mixing properties. Unlike any single-chain MCMC algorithm, upon reaching stationarity one of the PHS chains, which we call the “mother” chain, attains exact Monte Carlo sampling of the target distribution of interest. We empirically show that this translates in a dramatic improvement in the sampler’s performance with respect to single-chain MCMC algorithms. Convergence of the PHS joint transition kernel is proved and its relationships with single-chain samplers, Parallel Tempering (PT) and variable augmentation algorithms are discussed. We then provide two illustrative examples comparing the accuracy of PHS with

Pages: 57 pages
Date: 2009-09
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:ins:quaeco:qf0903

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