CosmoPMC: Cosmology Population Monte Carlo
arXiv:1101.0950
Abstract
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.
CosmoPMC user's guide, version v1.2. Replaced v1.1
References in corpus (3)
Cited by in corpus (10)
- Planck 2018 results. V. CMB power spectra and likelihoods
- Cosmology and Fundamental Physics with the Euclid Satellite
- Planck 2015 results. XI. CMB power spectra, likelihoods, and robustness of parameters
- The Cosmic Linear Anisotropy Solving System (CLASS) I: Overview
- CFHTLenS: Combined probe cosmological model comparison using 2D weak gravitational lensing
- The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Signs of neutrino mass in current cosmological datasets
- CosmoBolognaLib: C++ libraries for cosmological calculations
- Cosmological parameter inference with Bayesian statistics
- Weak Lensing Study in VOICE Survey I: Shear Measurement
- Exploring the constraints on cosmological models with CosmoEJS