Fundamentals and Recent Developments in Approximate Bayesian Computation

Jarno Lintusaari, Michael Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

70 Sitaatiot (Scopus)
180 Lataukset (Pure)


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.
DOI - pysyväislinkit
TilaJulkaistu - 2016
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

Sormenjälki Sukella tutkimusaiheisiin 'Fundamentals and Recent Developments in Approximate Bayesian Computation'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä