  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.
