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Hamiltonian Monte Carlo sampling to estimate past population dynamics using the skygrid coalescent model in a Bayesian phylogenetics framework.
Wellcome Open Res. 2020; 5:53.WO

Abstract

Nonparametric coalescent-based models are often employed to infer past population dynamics over time. Several of these models, such as the skyride and skygrid models, are equipped with a block-updating Markov chain Monte Carlo sampling scheme to efficiently estimate model parameters. The advent of powerful computational hardware along with the use of high-performance libraries for statistical phylogenetics has, however, made the development of alternative estimation methods feasible. We here present the implementation and performance assessment of a Hamiltonian Monte Carlo gradient-based sampler to infer the parameters of the skygrid model. The skygrid is a popular and flexible coalescent-based model for estimating population dynamics over time and is available in BEAST 1.10.5, a widely-used software package for Bayesian pylogenetic and phylodynamic analysis. Taking into account the increased computational cost of gradient evaluation, we report substantial increases in effective sample size per time unit compared to the established block-updating sampler. We expect gradient-based samplers to assume an increasingly important role for different classes of parameters typically estimated in Bayesian phylogenetic and phylodynamic analyses.

Authors+Show Affiliations

Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium.Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium.Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Herestraat 49, 3000, Leuven, Belgium.Departments of Biostatistics, Biomathematics and Human Genetics, University of California, Los Angeles, 695 Charles E. Young Drive, Los Angeles, California, 90095-1766, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32923688

Citation

Baele, Guy, et al. "Hamiltonian Monte Carlo Sampling to Estimate Past Population Dynamics Using the Skygrid Coalescent Model in a Bayesian Phylogenetics Framework." Wellcome Open Research, vol. 5, 2020, p. 53.
Baele G, Gill MS, Lemey P, et al. Hamiltonian Monte Carlo sampling to estimate past population dynamics using the skygrid coalescent model in a Bayesian phylogenetics framework. Wellcome Open Res. 2020;5:53.
Baele, G., Gill, M. S., Lemey, P., & Suchard, M. A. (2020). Hamiltonian Monte Carlo sampling to estimate past population dynamics using the skygrid coalescent model in a Bayesian phylogenetics framework. Wellcome Open Research, 5, 53. https://doi.org/10.12688/wellcomeopenres.15770.1
Baele G, et al. Hamiltonian Monte Carlo Sampling to Estimate Past Population Dynamics Using the Skygrid Coalescent Model in a Bayesian Phylogenetics Framework. Wellcome Open Res. 2020;5:53. PubMed PMID: 32923688.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Hamiltonian Monte Carlo sampling to estimate past population dynamics using the skygrid coalescent model in a Bayesian phylogenetics framework. AU - Baele,Guy, AU - Gill,Mandev S, AU - Lemey,Philippe, AU - Suchard,Marc A, Y1 - 2020/03/30/ PY - 2020/03/19/accepted PY - 2020/9/14/entrez PY - 2020/9/15/pubmed PY - 2020/9/15/medline KW - BEAGLE KW - BEAST KW - Bayesian skygrid KW - Hamiltonian Monte Carlo KW - Markov chain Monte Carlo KW - pathogen phylodynamics KW - phylogenetics SP - 53 EP - 53 JF - Wellcome open research JO - Wellcome Open Res VL - 5 N2 - Nonparametric coalescent-based models are often employed to infer past population dynamics over time. Several of these models, such as the skyride and skygrid models, are equipped with a block-updating Markov chain Monte Carlo sampling scheme to efficiently estimate model parameters. The advent of powerful computational hardware along with the use of high-performance libraries for statistical phylogenetics has, however, made the development of alternative estimation methods feasible. We here present the implementation and performance assessment of a Hamiltonian Monte Carlo gradient-based sampler to infer the parameters of the skygrid model. The skygrid is a popular and flexible coalescent-based model for estimating population dynamics over time and is available in BEAST 1.10.5, a widely-used software package for Bayesian pylogenetic and phylodynamic analysis. Taking into account the increased computational cost of gradient evaluation, we report substantial increases in effective sample size per time unit compared to the established block-updating sampler. We expect gradient-based samplers to assume an increasingly important role for different classes of parameters typically estimated in Bayesian phylogenetic and phylodynamic analyses. SN - 2398-502X UR - https://www.unboundmedicine.com/medline/citation/32923688/Hamiltonian_Monte_Carlo_sampling_to_estimate_past_population_dynamics_using_the_skygrid_coalescent_model_in_a_Bayesian_phylogenetics_framework_ L2 - https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/32923688/ DB - PRIME DP - Unbound Medicine ER -
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