scholarly journals Keeping the balance—Bridge sampling for marginal likelihood estimation in finite mixture, mixture of experts and Markov mixture models

2019 ◽  
Vol 33 (4) ◽  
pp. 706-733
Author(s):  
Sylvia Frühwirth-Schnatter
Keyword(s):  
Mixture Models ◽  
Marginal Likelihood ◽  
Likelihood Estimation ◽  
Finite Mixture ◽  
Mixture Of Experts ◽  
Bridge Sampling ◽  
Balance Bridge ◽  
Marginal Likelihood Estimation

scholarly journals Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12438
Author(s):  
Sebastian Höhna ◽  
Michael J. Landis ◽  
John P. Huelsenbeck
Keyword(s):  
Open Source Software ◽  
Marginal Likelihood ◽  
Substantial Reduction ◽  
Likelihood Estimation ◽  
Marginal Likelihoods ◽  
Parallelization Strategy ◽  
Sampling Algorithms ◽  
Primary Focus ◽  
Bayesian Phylogenetic Inference ◽  
Marginal Likelihood Estimation

In Bayesian phylogenetic inference, marginal likelihoods can be estimated using several different methods, including the path-sampling or stepping-stone-sampling algorithms. Both algorithms are computationally demanding because they require a series of power posterior Markov chain Monte Carlo (MCMC) simulations. Here we introduce a general parallelization strategy that distributes the power posterior MCMC simulations and the likelihood computations over available CPUs. Our parallelization strategy can easily be applied to any statistical model despite our primary focus on molecular substitution models in this study. Using two phylogenetic example datasets, we demonstrate that the runtime of the marginal likelihood estimation can be reduced significantly even if only two CPUs are available (an average performance increase of 1.96x). The performance increase is nearly linear with the number of available CPUs. We record a performance increase of 13.3x for cluster nodes with 16 CPUs, representing a substantial reduction to the runtime of marginal likelihood estimations. Hence, our parallelization strategy enables the estimation of marginal likelihoods to complete in a feasible amount of time which previously needed days, weeks or even months. The methods described here are implemented in our open-source software RevBayes which is available from http://www.RevBayes.com.

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