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Exact Bayesian inference for phylogenetic birth-death models

Abstract:

Motivation

Inferring the rates of change of a population from a reconstructed phylogeny of genetic sequences is a central problem in macro-evolutionary biology, epidemiology and many other disciplines. A popular solution involves estimating the parameters of a birth-death process (BDP), which links the shape of the phylogeny to its birth and death rates. Modern BDP estimators rely on random Markov chain Monte Carlo (MCMC) sampling to infer these rates. Such methods, while powerfu...

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Publication status:
Published
Peer review status:
Peer reviewed

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Files:
Publisher copy:
10.1093/bioinformatics/bty337

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Zoology
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Zoology
Oxford college:
Linacre College
Role:
Author
Publisher:
Oxford University Press Publisher's website
Journal:
Bioinformatics Journal website
Volume:
34
Issue:
21
Pages:
3638–3645
Publication date:
2018-04-26
Acceptance date:
2018-04-23
DOI:
EISSN:
1460-2059
ISSN:
1367-4811
Source identifiers:
844883
Pubs id:
pubs:844883
UUID:
uuid:07b14a1b-0e52-45f9-84ba-0a0882c11774
Local pid:
pubs:844883
Deposit date:
2018-04-30

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