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Article Dans Une Revue Physical Review D Année : 2009

Estimation of cosmological parameters using adaptive importance sampling

Résumé

We 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 wall-clock time for PMC. In the case of WMAP5 data, for example, the wall-clock time scale reduces from days for MCMC to hours using PMC on a cluster of processors. Other benefits of the PMC approach, along with potential difficulties in using the approach, are analyzed and discussed.
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hal-00450727 , version 1 (01-04-2022)

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Darren Wraith, Martin Kilbinger, Karim Benabed, Olivier Cappe, Jean-François Cardoso, et al.. Estimation of cosmological parameters using adaptive importance sampling. Physical Review D, 2009, 80, pp.023507. ⟨10.1103/PhysRevD.80.023507⟩. ⟨hal-00450727⟩
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