MontePython 3: boosted MCMC sampler and other features

TL;DR

MontePython 3 introduces advanced features like adaptive jumping factors and inverse Fisher matrix proposals to significantly enhance MCMC sampling efficiency in cosmological parameter inference, saving computational resources.

Contribution

The paper presents new algorithms for adaptive proposal tuning and Fisher matrix calculation, improving MontePython's performance over previous versions.

Findings

Speed up convergence of MCMC sampling.

Reduce CPU hours for complex runs.

Enhance functionality with new likelihoods and plotting options.

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.