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Phylogenetic inference via sequential Monte Carlo.

Bouchard-Côté A, Sankararaman S, Jordan MI - Syst. Biol. (2012)

Bottom Line: We provide a theoretical treatment of PosetSMC and also present experimental evaluation of PosetSMC on both synthetic and real data.The empirical results demonstrate that PosetSMC is a very promising alternative to MCMC, providing up to two orders of magnitude faster convergence.We discuss other factors favorable to the adoption of PosetSMC in phylogenetics, including its ability to estimate marginal likelihoods, its ready implementability on parallel and distributed computing platforms, and the possibility of combining with MCMC in hybrid MCMC-SMC schemes.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z2, Canada.

ABSTRACT
Bayesian inference provides an appealing general framework for phylogenetic analysis, able to incorporate a wide variety of modeling assumptions and to provide a coherent treatment of uncertainty. Existing computational approaches to bayesian inference based on Markov chain Monte Carlo (MCMC) have not, however, kept pace with the scale of the data analysis problems in phylogenetics, and this has hindered the adoption of bayesian methods. In this paper, we present an alternative to MCMC based on Sequential Monte Carlo (SMC). We develop an extension of classical SMC based on partially ordered sets and show how to apply this framework--which we refer to as PosetSMC--to phylogenetic analysis. We provide a theoretical treatment of PosetSMC and also present experimental evaluation of PosetSMC on both synthetic and real data. The empirical results demonstrate that PosetSMC is a very promising alternative to MCMC, providing up to two orders of magnitude faster convergence. We discuss other factors favorable to the adoption of PosetSMC in phylogenetics, including its ability to estimate marginal likelihoods, its ready implementability on parallel and distributed computing platforms, and the possibility of combining with MCMC in hybrid MCMC-SMC schemes. Software for PosetSMC is available at http://www.stat.ubc.ca/ bouchard/PosetSMC.

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Results on ribosomal RNA data (Cannone et al. 2002) on different tree sizes, comparing the log likelihood of the minimum Bayes risk reconstruction from SMC and MCMC approximations, as a function of the running time (in units of the number of peeling recursions on a log scale).
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fig9: Results on ribosomal RNA data (Cannone et al. 2002) on different tree sizes, comparing the log likelihood of the minimum Bayes risk reconstruction from SMC and MCMC approximations, as a function of the running time (in units of the number of peeling recursions on a log scale).

Mentions: We show the results in Figure 9. As in the synthetic data experiments, we found that the PosetSMC sampler required around two orders of magnitude less time to converge to a good approximation of the posterior. Moreover, the advantage of PosetSMC over MCMC becomes more pronounced as the number of taxa increases. For large numbers of iterations and particles, the MCMC sampler slightly outperformed the PosetSMC sampler on the real data we used.FIGURE 9.


Phylogenetic inference via sequential Monte Carlo.

Bouchard-Côté A, Sankararaman S, Jordan MI - Syst. Biol. (2012)

Results on ribosomal RNA data (Cannone et al. 2002) on different tree sizes, comparing the log likelihood of the minimum Bayes risk reconstruction from SMC and MCMC approximations, as a function of the running time (in units of the number of peeling recursions on a log scale).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376373&req=5

fig9: Results on ribosomal RNA data (Cannone et al. 2002) on different tree sizes, comparing the log likelihood of the minimum Bayes risk reconstruction from SMC and MCMC approximations, as a function of the running time (in units of the number of peeling recursions on a log scale).
Mentions: We show the results in Figure 9. As in the synthetic data experiments, we found that the PosetSMC sampler required around two orders of magnitude less time to converge to a good approximation of the posterior. Moreover, the advantage of PosetSMC over MCMC becomes more pronounced as the number of taxa increases. For large numbers of iterations and particles, the MCMC sampler slightly outperformed the PosetSMC sampler on the real data we used.FIGURE 9.

Bottom Line: We provide a theoretical treatment of PosetSMC and also present experimental evaluation of PosetSMC on both synthetic and real data.The empirical results demonstrate that PosetSMC is a very promising alternative to MCMC, providing up to two orders of magnitude faster convergence.We discuss other factors favorable to the adoption of PosetSMC in phylogenetics, including its ability to estimate marginal likelihoods, its ready implementability on parallel and distributed computing platforms, and the possibility of combining with MCMC in hybrid MCMC-SMC schemes.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z2, Canada.

ABSTRACT
Bayesian inference provides an appealing general framework for phylogenetic analysis, able to incorporate a wide variety of modeling assumptions and to provide a coherent treatment of uncertainty. Existing computational approaches to bayesian inference based on Markov chain Monte Carlo (MCMC) have not, however, kept pace with the scale of the data analysis problems in phylogenetics, and this has hindered the adoption of bayesian methods. In this paper, we present an alternative to MCMC based on Sequential Monte Carlo (SMC). We develop an extension of classical SMC based on partially ordered sets and show how to apply this framework--which we refer to as PosetSMC--to phylogenetic analysis. We provide a theoretical treatment of PosetSMC and also present experimental evaluation of PosetSMC on both synthetic and real data. The empirical results demonstrate that PosetSMC is a very promising alternative to MCMC, providing up to two orders of magnitude faster convergence. We discuss other factors favorable to the adoption of PosetSMC in phylogenetics, including its ability to estimate marginal likelihoods, its ready implementability on parallel and distributed computing platforms, and the possibility of combining with MCMC in hybrid MCMC-SMC schemes. Software for PosetSMC is available at http://www.stat.ubc.ca/ bouchard/PosetSMC.

Show MeSH
Related in: MedlinePlus