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VMCMC: A graphical and statistical analysis tool for Markov chain Monte Carlo traces
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST). KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Centra, SeRC - Swedish e-Science Research Centre.
KTH, Skolan för informations- och kommunikationsteknik (ICT).
KTH, Skolan för informations- och kommunikationsteknik (ICT).
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST). KTH, Centra, Science for Life Laboratory, SciLifeLab. KTH, Centra, SeRC - Swedish e-Science Research Centre.ORCID-id: 0000-0002-6664-1607
Vise andre og tillknytning
2017 (engelsk)Inngår i: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 18, nr 1, artikkel-id 97Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Background: MCMC-based methods are important for Bayesian inference of phylogeny and related parameters. Although being computationally expensive, MCMC yields estimates of posterior distributions that are useful for estimating parameter values and are easy to use in subsequent analysis. There are, however, sometimes practical difficulties with MCMC, relating to convergence assessment and determining burn-in, especially in large-scale analyses. Currently, multiple software are required to perform, e.g., convergence, mixing and interactive exploration of both continuous and tree parameters. Results: We have written a software called VMCMC to simplify post-processing of MCMC traces with, for example, automatic burn-in estimation. VMCMC can also be used both as a GUI-based application, supporting interactive exploration, and as a command-line tool suitable for automated pipelines. Conclusions: VMCMC is a free software available under the New BSD License. Executable jar files, tutorial manual and source code can be downloaded from https://bitbucket.org/rhali/visualmcmc/.

sted, utgiver, år, opplag, sider
BioMed Central, 2017. Vol. 18, nr 1, artikkel-id 97
Emneord [en]
Convergence, Markov chain Monte Carlo, Metropolis-Hastings, Phylogenetics, Software, Visualization
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-208116DOI: 10.1186/s12859-017-1505-3ISI: 000397489700003PubMedID: 28187712Scopus ID: 2-s2.0-85012066451OAI: oai:DiVA.org:kth-208116DiVA, id: diva2:1106300
Forskningsfinansiär
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish e‐Science Research Center
Merknad

QC 20170607

Tilgjengelig fra: 2017-06-07 Laget: 2017-06-07 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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Ali, Raja HashimBark, MikaelMiró, JorgeMuhammad, Sayyed AuwnZubair, Syed M.
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