MontePython 3: boosted MCMC sampler and other features
Abstract
MontePython is a parameter inference package for cosmology. We present the latest development of the code over the past couple of years. We explain, in particular, two new ingredients both contributing to improve the performance of Metropolis-Hastings sampling: an adaptation algorithm for the jumping factor, and a calculation of the inverse Fisher matrix, which can be used as a proposal density. We present several examples to show that these features speed up convergence and can save many hundreds of CPU-hours in the case of difficult runs, with a poor prior knowledge of the covariance matrix. We also summarise all the functionalities of MontePython in the current release, including new likelihoods and plotting options.
Cite
@article{arxiv.1804.07261,
title = {MontePython 3: boosted MCMC sampler and other features},
author = {Thejs Brinckmann and Julien Lesgourgues},
journal= {arXiv preprint arXiv:1804.07261},
year = {2018}
}
Comments
17 pages, 2 figures, code available at https://github.com/brinckmann/montepython_public, v2: URL corrected, refs added