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dc.contributor.authorWraith, Darren
dc.contributor.authorCappé, Olivier
dc.contributor.authorCardoso, Jean-François
dc.contributor.authorFort, Gersende
dc.contributor.authorPrunet, Simon
dc.contributor.authorKilbinger, Martin
dc.contributor.authorBenabed, Karim
dc.contributor.authorRobert, Christian P.
dc.descriptionArticle 023507en
dc.subjectCosmological problemsen
dc.titleEstimation of cosmological parameters using adaptive importance samplingen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe present a Bayesian sampling algorithm called adaptive importance sampling or Population Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to considerably reduce the wall-clock time required for sampling, along with providing other benefits. To assess the performance of the approach for cosmological problems, we use simulated and actual data consisting of CMB anisotropies, supernovae of type Ia, and weak cosmological lensing, and provide a comparison of results to those obtained using state-of-the-art Markov Chain Monte Carlo (MCMC). For both types of data sets, we find comparable parameter estimates for PMC and MCMC, with the advantage of a significantly lower computational time for PMC. In the case of WMAP5 data, for example, the wall-clock time reduces from several days for MCMC to a few hours using PMC on a cluster of processors. Other benefits of the PMC approach, along with potential difficulties in using the approach, are analysed and discussed.en
dc.relation.isversionofjnlnamePhysical Review. D, Particles, Fields, Gravitation and Cosmology
dc.relation.isversionofjnlpublisherAmerican Physical Societyen
dc.subject.ddclabelSciences connexes (physique, astrophysique)en

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