Estimation of cosmological parameters using adaptive importance sampling

Wraith, Darren, Kilbinger, Martin, Benabed, Karim, Cappé, Olivier, Cardoso, Jean-François, Fort, Gersende, Prunet, Simon, & Robert, Christian P. (2009) Estimation of cosmological parameters using adaptive importance sampling. Physical Review E, 80(2), Article Number-023507.

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Abstract

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.

Impact and interest:

40 citations in Scopus
46 citations in Web of Science®
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ID Code: 92572
Item Type: Journal Article
Refereed: Yes
DOI: 10.1103/PhysRevD.80.023507
ISSN: 1550-2368
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Copyright Owner: Copyright 2009 American Physical Society
Deposited On: 02 Feb 2016 05:24
Last Modified: 26 Jun 2017 14:46

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