{"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/19-dubious-ways-to-compute-the-marginal","title":"19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology","arxiv_id":"1811.11804","date":"2018-11-28","proceeding":null,"authors":["Mathieu Fourment","Andrew F. Magee","Chris Whidden","Arman Bilge","Frederick A. Matsen IV","Vladimir N. Minin"],"abstract":"The marginal likelihood of a model is a key quantity for assessing the\nevidence provided by the data in support of a model. The marginal likelihood is\nthe normalizing constant for the posterior density, obtained by integrating the\nproduct of the likelihood and the prior with respect to model parameters. Thus,\nthe computational burden of computing the marginal likelihood scales with the\ndimension of the parameter space. In phylogenetics, where we work with tree\ntopologies that are high-dimensional models, standard approaches to computing\nmarginal likelihoods are very slow. Here we study methods to quickly compute\nthe marginal likelihood of a single fixed tree topology. We benchmark the speed\nand accuracy of 19 different methods to compute the marginal likelihood of\nphylogenetic topologies on a suite of real datasets. These methods include\nseveral new ones that we develop explicitly to solve this problem, as well as\nexisting algorithms that we apply to phylogenetic models for the first time.\nAltogether, our results show that the accuracy of these methods varies widely,\nand that accuracy does not necessarily correlate with computational burden. Our\nnewly developed methods are orders of magnitude faster than standard\napproaches, and in some cases, their accuracy rivals the best established\nestimators.","url_abs":"http://arxiv.org/abs/1811.11804v1","url_pdf":"http://arxiv.org/pdf/1811.11804v1.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":"abstracts"},"code_links":[{"paper_slug":"19-dubious-ways-to-compute-the-marginal","repo_url":"https://github.com/4ment/marginal-experiments","is_official":1,"mentioned_in_paper":1,"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}