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This reflection presents two case studies on Ebola virus and HIV, showing that topology-specific diagnostics can reveal information absent from conventional methods and that diagnostic choices influence perceived MCMC convergence and mixing. The work emphasizes running multiple replicate analyses and carefully assessing topological convergence using their outputs.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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diagnostic choices affect conclusions about convergence and mixing?",{"text":72,"@type":64},"Running multiple replicate analyses and carefully assessing topological convergence can change inference outcomes regarding MCMC convergence and mixing, highlighting that diagnostic selection matters.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},156018,1787942776,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & 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convergence in Bayesian phylogenetic inference  \nPermalink  \n[https://escholarship.org/uc/item/7tx803mj](https://escholarship.org/uc/item/7tx803mj)  \nJournal  \nVirus Evolution, 10(1)  \nISSN  \n2057-1577  \nAuthors  \nBrusselmans, Marius  \nCarvalho, Luiz Max Hong, Samuel Let al.  \nPublication Date  \n2024-11-12  \nDOI  \n10.1093/ve/veae081  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial License, available at [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nVirus Evolution, 2024, 10(1), veae081  \nDOI: [https://doi.org/10.1093/ve/veae081](https://doi.org/10.1093/ve/veae081)  \nAdvance access publication date: 15 October 2024  \nReflections  \nOn the importance of assessing topological convergence in Bayesian phylogenetic inference  \nMarius Brusselmans1,* , Luiz Max Carvalho2 , Samuel L. Hong1 , Jiansi Gao3 , Frederick A. Matsen IV4,5,6 , Andrew Rambaut7 , Philippe Lemey1 , Marc A. Suchard8 , Gytis Dudas9 , Guy Baele1  \n1Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium  \n2School of Applied Mathematics, Getulio Vargas Foundation, Praia de Botafogo, 190, 22250-900 Rio de Janeiro, Brazil  \n3Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109, United States  \n4Howard Hughes Medical Institute, Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States  \n5Department of Genome Sciences, University of Washington, Seattle, Washington, United States  \n6Department of Statistics, University of Washington, Seattle, Washington, United States  \n7Institute of Ecology and Evolution, University of Edinburgh, Edinburgh EH9 3FL, United Kingdom  \n8Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA 90095, USA  \n9Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania  \n*Corresponding author. Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium. [E-mail:](E-mail: marius.brusselmans@kuleuven.be)[ marius.brusselmans@kuleuven.be](E-mail: marius.brusselmans@kuleuven.be)  \nAbstract  \nModern phylogenetics research is often performed within a Bayesian framework, using sampling algorithms such as Markov chain Monte Carlo (MCMC) to approximate the posterior distribution. These algorithms require careful evaluation of the quality of the generated samples. Within the field of phylogenetics, one frequently adopted diagnostic approach is to evaluate the effective sample size and to investigate trace graphs of the sampled parameters. A major limitation of these approaches is that they are developed for continuous parameters and therefore incompatible with a crucial parameter in these inferences: the tree topology. Several recent advancements have aimed at extending these diagnostics to topological space. In this reflection paper, we present two case studies—one on Ebola virus and one on HIV—illustrating how these topological diagnostics can contain information not found in standard diagnostics, and how decisions regarding which of these diagnostics to compute can impact inferences regarding MCMC convergence and mixing. Our results show the importance of running multiple replicate analyses and of carefully assessing topological convergence using the output of these replicate analyses. To this end, we illustrate different ways of assessing and visualizing the topological convergence of these replicates. Given the major importance of detecting convergence and mixing issues in Bayesian phylogenetic analyses, the lack of a unified approach to this problem warrants further action, especiall","cbCainlsZUFf8oHR","https://ap.wps.com/l/cbCainlsZUFf8oHR","pdf",21263784,14,"English","# Abstract\n# Introduction\n## Background\n## Recent research on topology diagnostics","[{\"question\":\"Why are standard Bayesian phylogenetic diagnostics insufficient for assessing MCMC quality?\",\"answer\":\"They are largely developed for continuous parameters and therefore do not directly diagnose the tree topology, a key discrete parameter in phylogenetic inference.\"},{\"question\":\"What are the main diagnostics discussed for tree topology in this reflection paper?\",\"answer\":\"The paper focuses on topological convergence diagnostics, including methods for assessing and visualizing convergence across replicate analyses.\"},{\"question\":\"How do replicate analyses and diagnostic choices affect conclusions about convergence and mixing?\",\"answer\":\"Running multiple replicate analyses and carefully assessing topological convergence can change inference outcomes regarding MCMC convergence and mixing, highlighting that diagnostic selection matters.\"}]","On the importance of assessing topological convergence in Bayesian phylogenetic inference | PDF",35]