814272
doi
10.5281/zenodo.814272
oai:zenodo.org:814272
Bilge, Arman
Fred Hutchinson Cancer Research Center
Zhang, Cheng
Fred Hutchinson Cancer Research Center
Matsen IV, Frederick A.
Probabilistic Path Hamiltonian Monte Carlo for Bayesian Phylogenetic Inference
Dinh, Vu
Fred Hutchinson Cancer Research Center
arxiv:arXiv:1702.07814
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
phylogenetics
Bayesian inference
Hamiltonian Monte Carlo
<p>Hamiltonian Monte Carlo (HMC) is an efficient and effective means of sampling posterior distributions on Euclidean space, which has been extended to manifolds with boundary. However, some applications require an extension to more general spaces. For example, phylogenetic (evolutionary) trees are defined in terms of both a discrete graph and associated continuous parameters; although one can represent these aspects using a single connected space, this rather complex space is not suitable for existing HMC algorithms. In this paper, we develop Probabilistic Path HMC (PPHMC) as a first step to sampling distributions on spaces with intricate combinatorial structure. We define PPHMC on orthant complexes, show that the resulting Markov chain is ergodic, and provide a promising implementation for the case of phylogenetic trees in open-source software. We also show that a surrogate function to ease the transition across a boundary on which the log-posterior has discontinuous derivatives can greatly improve efficiency.</p>
Zenodo
2017-06-26
info:eu-repo/semantics/conferencePoster
814271
1579541927.5141
483994
md5:340d74dd12f69bfedf7b112bfb35290a
https://zenodo.org/records/814272/files/evol17.pdf
public
arXiv:1702.07814
Is supplemented by
arxiv
10.5281/zenodo.814271
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