Fundamentals and Recent Developments in Approximate Bayesian Computation.
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.]
Department of Computer Science, Aalto University, Espoo, Finland.|Helsinki Institute for Information Technology HIIT, Espoo, Finland.
Jarno
J
Lintusaari
Department of Computer Science, Aalto University, Espoo, Finland.|Helsinki Institute for Information Technology HIIT, Espoo, Finland.|Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
Michael U
MU
Gutmann
Department of Computer Science, Aalto University, Espoo, Finland.|Helsinki Institute for Information Technology HIIT, Espoo, Finland.
Ritabrata
R
Dutta
Department of Computer Science, Aalto University, Espoo, Finland.|Helsinki Institute for Information Technology HIIT, Espoo, Finland.
Samuel
S
Kaski
Helsinki Institute for Information Technology HIIT, Espoo, Finland.|Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.|Department of Biostatistics, University of Oslo, Oslo, Norway
Jukka
J
Corander
D000465:Algorithms; D001499:Bayes Theorem; D002965:Classification; D008954:Models, Biological; D010802:Phylogeny