Parallel and interacting Markov chain Monte Carlo algorithm |
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Affiliation: | 1. CISUC, Department of Informatics Engineering, University of Coimbra, Portugal;2. Mathematical Institute, University of Kaiserslautern, Germany |
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Abstract: | In many situations it is important to be able to propose N independent realizations of a given distribution law. We propose a strategy for making N parallel Monte Carlo Markov chains (MCMC) interact in order to get an approximation of an independent N-sample of a given target law. In this method each individual chain proposes candidates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of N target measures. Compared to independent parallel chains this method is more time consuming, but we show through examples that it possesses many advantages. This approach is applied to a biomass evolution model. |
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