• Corpus ID: 237353627

Lagged couplings diagnose Markov chain Monte Carlo phylogenetic inference

  title={Lagged couplings diagnose Markov chain Monte Carlo phylogenetic inference},
  author={Luke J Kelly and Robin J. Ryder and Gr'egoire Clart'e},
Phylogenetic inference is an intractable statistical problem on a complex sample space. Markov chain Monte Carlo methods are the primary tool for Bayesian phylogenetic inference, but it is challenging to construct efficient schemes to explore the associated posterior distribution and to then assess their convergence. Building on recent work developing couplings of Monte Carlo algorithms, we describe a procedure to couple Markov Chains targeting a posterior distribution over a space of… 

Figures and Tables from this paper


Unbiased Markov chain Monte Carlo methods with couplings
The theoretical validity of the estimators proposed and their efficiency relative to the underlying MCMC algorithms are established and the performance and limitations of the method are illustrated.
An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics.
We describe an "embarrassingly parallel" method for Bayesian phylogenetic inference, annealed Sequential Monte Carlo (SMC), based on recent advances in the SMC literature such as adaptive
Estimating Convergence of Markov chains with L-Lag Couplings
L-lag couplings are introduced to generate computable, non-asymptotic upper bound estimates for the total variation or the Wasserstein distance of general Markov chains.
Systematic Exploration of the High Likelihood Set of Phylogenetic Tree Topologies
This paper presents an efficient parallelized method to map out the high likelihood set of phylogenetic tree topologies via systematic search, and shows that the normalized topology likelihoods are a useful proxy for the Bayesian posterior probability of those topologies.
RWTY (R We There Yet): An R Package for Examining Convergence of Bayesian Phylogenetic Analyses.
Bayesian inference using Markov chain Monte Carlo (MCMC) has become one of the primary methods used to infer phylogenies from sequence data. Assessing convergence is a crucial component of these
MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space
The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly, and provides more output options than previously, including samples of ancestral states, site rates, site dN/dS rations, branch rates, and node dates.
Mrbayes version 3.2 manual: Tutorials and model summaries
  • https://github. com/NBISweden/MrBayes/blob/develop/doc/manual/Manual_MrBayes_v3.2.pdf,
  • 2020
19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology.
This work benchmarks the speed and accuracy of 19 different methods to compute the marginal likelihood of phylogenetic topologies on a suite of real datasets under the JC69 model, and shows that the accuracy of these methods varies widely, and that accuracy does not necessarily correlate with computational burden.
Evolutionary dynamics of language systems
It is suggested that different subsystems of language have differing dynamics and that careful, nuanced models of language change will be needed to extract deeper signal from the noise of parallel evolution, areal readaptation, and contact.
Estimating the Effective Sample Size of Tree Topologies from Bayesian Phylogenetic Analyses
These methods are combined with two new diagnostic plots for assessing posterior samples of tree topologies, and provide new ways to assess the mixing and convergence of phylogenetic treetopologies in Bayesian MCMC analyses.