Estimation of cosmological parameters using adaptive importance sampling

TL;DR

This paper introduces an adaptive importance sampling algorithm called Population Monte Carlo (PMC) for cosmological parameter estimation, demonstrating comparable accuracy to MCMC but with significantly reduced computational time, especially for large datasets.

Contribution

The paper presents a new parallelizable Bayesian sampling method, PMC, optimized for cosmological data analysis, offering faster results than traditional MCMC techniques.

Findings

PMC achieves similar parameter estimates as MCMC.

PMC significantly reduces computational time, e.g., from days to hours.

PMC is easily parallelizable and suitable for large cosmological datasets.

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 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.