Estimation of cosmological parameters using adaptive importance sampling
Wraith, Darren; Cappé, Olivier; Cardoso, Jean-François; Fort, Gersende; Prunet, Simon; Kilbinger, Martin; Benabed, Karim; Robert, Christian P. (2009), Estimation of cosmological parameters using adaptive importance sampling, Physical Review. D, Particles, Fields, Gravitation and Cosmology, 80, 2. http://dx.doi.org/10.1103/PhysRevD.80.023507
Type
Article accepté pour publication ou publiéExternal document link
http://hal.archives-ouvertes.fr/hal-00365944/en/Date
2009-03Journal name
Physical Review. D, Particles, Fields, Gravitation and CosmologyVolume
80Number
2Publisher
American Physical Society
Publication identifier
Metadata
Show full item recordAuthor(s)
Wraith, DarrenCappé, Olivier

Cardoso, Jean-François

Fort, Gersende

Prunet, Simon

Kilbinger, Martin
Benabed, Karim
Robert, Christian P.
Abstract (EN)
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 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.Subjects / Keywords
PMC; MCMC; Cosmological problemsRelated items
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