{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/montepython-3-boosted-mcmc-sampler-and-other","title":"MontePython 3: boosted MCMC sampler and other features","arxiv_id":"1804.07261","date":"2018-04-19","proceeding":null,"authors":["Thejs Brinckmann","Julien Lesgourgues"],"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.","url_abs":"http://arxiv.org/abs/1804.07261v2","url_pdf":"http://arxiv.org/pdf/1804.07261v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"montepython-3-boosted-mcmc-sampler-and-other","repo_url":"https://github.com/brinckmann/montepython_public","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"montepython-3-boosted-mcmc-sampler-and-other","repo_url":"https://github.com/AarhusCosmology/montepython_public","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"montepython-3-boosted-mcmc-sampler-and-other","repo_url":"https://github.com/BStoelzner/montepython_2cosmos_public","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"montepython-3-boosted-mcmc-sampler-and-other","repo_url":"https://github.com/PoulinV/montepython_public_v3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"montepython-3-boosted-mcmc-sampler-and-other","repo_url":"https://github.com/aaronglanville/montepython_public","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"montepython-3-boosted-mcmc-sampler-and-other","repo_url":"https://github.com/adammoss/montepython_public","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"montepython-3-boosted-mcmc-sampler-and-other","repo_url":"https://github.com/bstoelzner/montepython_public","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"montepython-3-boosted-mcmc-sampler-and-other","repo_url":"https://github.com/fkoehlin/montepython_2cosmos_public","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"montepython-3-boosted-mcmc-sampler-and-other","repo_url":"https://github.com/gaetanfacchinetti/montepython_public","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"montepython-3-boosted-mcmc-sampler-and-other","repo_url":"https://github.com/genye00/montepython-3.5_lens","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"montepython-3-boosted-mcmc-sampler-and-other","repo_url":"https://github.com/ivandebono/montepython_public_3.2dev_Python3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"montepython-3-boosted-mcmc-sampler-and-other","repo_url":"https://github.com/wilmarcardonac/montepython_public","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}