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.

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 publié

External document link

http://hal.archives-ouvertes.fr/hal-00365944/en/

Date

2009-03

Journal name

Physical Review. D, Particles, Fields, Gravitation and Cosmology

Volume

Number

Publisher

American Physical Society

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 problems