Fundamentals and Recent Developments in Approximate Bayesian Computation.
Syst Biol
; 66(1): e66-e82, 2017 01 01.
Article
em En
| MEDLINE
| ID: mdl-28175922
ABSTRACT
Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions by only requiring that sampling from a model is possible. We explain here the fundamentals of ABC, review the classical algorithms, and highlight recent developments. [ABC; approximate Bayesian computation; Bayesian inference; likelihood-free inference; phylogenetics; simulator-based models; stochastic simulation models; tree-based models.]
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Filogenia
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Classificação
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Modelos Biológicos
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article