{"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/model-selection-and-parameter-inference-in","title":"Model selection and parameter inference in phylogenetics using Nested Sampling","arxiv_id":"1703.05471","date":"2018-04-10","proceeding":null,"authors":[],"abstract":"Bayesian inference methods rely on numerical algorithms for both model\nselection and parameter inference. In general, these algorithms require a high\ncomputational effort to yield reliable estimates. One of the major challenges\nin phylogenetics is the estimation of the marginal likelihood. This quantity is\ncommonly used for comparing different evolutionary models, but its calculation,\neven for simple models, incurs high computational cost. Another interesting\nchallenge relates to the estimation of the posterior distribution. Often, long\nMarkov chains are required to get sufficient samples to carry out parameter\ninference, especially for tree distributions. In general, these problems are\naddressed separately by using different procedures. Nested sampling (NS) is a\nBayesian computation algorithm which provides the means to estimate marginal\nlikelihoods together with their uncertainties, and to sample from the posterior\ndistribution at no extra cost. The methods currently used in phylogenetics for\nmarginal likelihood estimation lack in practicality due to their dependence on\nmany tuning parameters and the inability of most implementations to provide a\ndirect way to calculate the uncertainties associated with the estimates. To\naddress these issues, we introduce NS to phylogenetics. Its performance is\nassessed under different scenarios and compared to established methods. We\nconclude that NS is a competitive and attractive algorithm for phylogenetic\ninference. An implementation is available as a package for BEAST 2 under the\nLGPL licence, accessible at https://github.com/BEAST2-Dev/nested-sampling.","url_abs":"http://arxiv.org/abs/1703.05471v3","url_pdf":"http://arxiv.org/pdf/1703.05471v3.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":"model-selection-and-parameter-inference-in","repo_url":"https://github.com/BEAST2-Dev/nested-sampling","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}