ASCL.net

Astrophysics Source Code Library

Making codes discoverable since 1999

Searching for codes credited to 'Benabed, Karim'

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[ascl:1212.006] CosmoPMC: Cosmology sampling with Population Monte Carlo

CosmoPMC is a Monte-Carlo sampling method to explore the likelihood of various cosmological probes. The sampling engine is implemented with the package pmclib. It is called Population MonteCarlo (PMC), which is a novel technique to sample from the posterior. PMC is an adaptive importance sampling method which iteratively improves the proposal to approximate the posterior. This code has been introduced, tested and applied to various cosmology data sets.

[ascl:1307.002] Monte Python: Monte Carlo code for CLASS in Python

Monte Python is a parameter inference code which combines the flexibility of the python language and the robustness of the cosmological code CLASS into a simple and easy to manipulate Monte Carlo Markov Chain code.

This version has been archived and replaced by MontePython 3 (ascl:1805.027).

[ascl:1805.027] MontePython 3: Parameter inference code for cosmology

MontePython 3 provides numerous ways to explore parameter space using Monte Carlo Markov Chain (MCMC) sampling, including Metropolis-Hastings, Nested Sampling, Cosmo Hammer, and a Fisher sampling method. This improved version of the Monte Python (ascl:1307.002) parameter inference code for cosmology offers new ingredients that improve the performance of Metropolis-Hastings sampling, speeding up convergence and offering significant time improvement in difficult runs. Additional likelihoods and plotting options are available, as are post-processing algorithms such as Importance Sampling and Adding Derived Parameter.