CosmoPMC: Cosmology Population Monte Carlo
  We present the public release of the Bayesian sampling algorithm for
cosmology, CosmoPMC (Cosmology Population Monte Carlo). CosmoPMC explores the
parameter space of various cosmological probes, and also provides a robust
estimate of the Bayesian evidence. CosmoPMC is based on an adaptive importance
sampling method called Population Monte Carlo (PMC). Various cosmology
likelihood modules are implemented, and new modules can be added easily. The
importance-sampling algorithm is written in C, and fully parallelised using the
Message Passing Interface (MPI). Due to very little overhead, the wall-clock
time required for sampling scales approximately with the number of CPUs. The
CosmoPMC package contains post-processing and plotting programs, and in
addition a Monte-Carlo Markov chain (MCMC) algorithm. The sampling engine is
implemented in the library pmclib, and can be used independently. The software
is available for download at http://www.cosmopmc.info.
