Limits...
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.

Show MeSH
An overview of the PosetSMC algorithmic framework. A PosetSMC algorithm maintains a set of partial states (three partial states are shown in the leftmost column in the figure; each partial state is a forest over the leaves A, B, C, and D). Associated with each partial state is a positive-valued weight. The algorithm iterates the following three steps: (i) resample from the weighted partial states to obtain an unweighted set of partial states, (ii) propose an extension of each partial state to a new partial state in which two trees in the forest have been connected, and (iii) calculate the weights associated with the new partial states.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3376373&req=5

fig1: An overview of the PosetSMC algorithmic framework. A PosetSMC algorithm maintains a set of partial states (three partial states are shown in the leftmost column in the figure; each partial state is a forest over the leaves A, B, C, and D). Associated with each partial state is a positive-valued weight. The algorithm iterates the following three steps: (i) resample from the weighted partial states to obtain an unweighted set of partial states, (ii) propose an extension of each partial state to a new partial state in which two trees in the forest have been connected, and (iii) calculate the weights associated with the new partial states.

Mentions: Figure 1 presents an overview of the overall PosetSMC algorithmic framework. It will be useful to refer to this figure as we proceed through the formal specification of the framework.FIGURE 1.


Phylogenetic inference via sequential Monte Carlo.

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

An overview of the PosetSMC algorithmic framework. A PosetSMC algorithm maintains a set of partial states (three partial states are shown in the leftmost column in the figure; each partial state is a forest over the leaves A, B, C, and D). Associated with each partial state is a positive-valued weight. The algorithm iterates the following three steps: (i) resample from the weighted partial states to obtain an unweighted set of partial states, (ii) propose an extension of each partial state to a new partial state in which two trees in the forest have been connected, and (iii) calculate the weights associated with the new partial states.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

fig1: An overview of the PosetSMC algorithmic framework. A PosetSMC algorithm maintains a set of partial states (three partial states are shown in the leftmost column in the figure; each partial state is a forest over the leaves A, B, C, and D). Associated with each partial state is a positive-valued weight. The algorithm iterates the following three steps: (i) resample from the weighted partial states to obtain an unweighted set of partial states, (ii) propose an extension of each partial state to a new partial state in which two trees in the forest have been connected, and (iii) calculate the weights associated with the new partial states.
Mentions: Figure 1 presents an overview of the overall PosetSMC algorithmic framework. It will be useful to refer to this figure as we proceed through the formal specification of the framework.FIGURE 1.

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