2009/03/04 by Darren Wraith, M. Kilbinger, Martin Kilbinger +9
Mathematics · Physics and Astronomy · Social Sciences · #Cosmology and Gravitation Theories #Insurance, Mortality, Demography, Risk Management #Markov Chains and Monte Carlo Methods #astro-ph.CO #stat.CO
paper · pdf · doi:10.1103/physrevd.80.023507
published as Phys.Rev.D80:023507,2009 · 17 pages, 11 figures
arxiv created 2009/03/04 · openalex publication_date 2009/07/10 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.