VMCMC：一种用于马尔可夫链蒙特卡罗轨迹的图形和统计分析工具。

VMCMC: a graphical and statistical analysis tool for Markov chain Monte Carlo traces.

作者信息

Ali Raja H, Bark Mikael, Miró Jorge, Muhammad Sayyed A, Sjöstrand Joel, Zubair Syed M, Abbas Raja M, Arvestad Lars

机构信息

KTH Royal Institute of Technology, Swedish e-Science Research Centre, Science for Life Laboratory, School of Computer Science and Communication, Solna, SE-171 77, Sweden.

KTH Royal Institute of Technology, School of Information and Communication Technology, Kista, SE-164 40, Sweden.

出版信息

BMC Bioinformatics. 2017 Feb 10;18(1):97. doi: 10.1186/s12859-017-1505-3.

DOI:10.1186/s12859-017-1505-3

PMID:28187712

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5301390/

Abstract

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/ .

摘要

背景

基于马尔可夫链蒙特卡洛（MCMC）的方法对于系统发育及相关参数的贝叶斯推断非常重要。尽管计算成本高昂，但MCMC能产生后验分布的估计值，这些估计值对于估计参数值很有用，并且便于在后续分析中使用。然而，MCMC有时存在实际困难，与收敛评估和确定预烧期有关，特别是在大规模分析中。目前，需要多个软件来执行，例如，对连续参数和树形参数进行收敛、混合和交互式探索。