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Author (up) Campillo, F.; Rakotozafy, R.; Rossi, V. openurl 
  Title Parallel and interacting Markov chain Monte Carlo algorithm Type Journal Article
  Year 2009 Publication Mathematics and Computers in Simulation Abbreviated Journal Math. Comput. Simul.  
  Volume 79 Issue 12 Pages 3424-3433  
  Keywords Markov chain Monte Carlo method; Interacting chains; Hidden Markov model  
  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. (C) 2009 IMACS. Published by Elsevier B.V. All rights reserved.  
  Address [Rossi, Vivien] CIRAD, Res Unit, Montpellier, France, Email: Fabien.Campillo@inria.fr  
  Corporate Author Thesis  
  Publisher ELSEVIER SCIENCE BV Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0378-4754 ISBN Medium  
  Area Expedition Conference  
  Notes ISI:000269289100006 Approved no  
  Call Number EcoFoG @ eric.marcon @ Serial 197  
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