Approximate Bayesian computation (ABC) is a family of computational techniques in Bayesian statistics. These simulation techniques operate on summary data (such as population mean, or variance) to make broad inferences with less computation than might be required if all available data were analyzed in detail. They are especially useful in situations where evaluation of the likelihood is computationally prohibitive, or whenever suitable likelihoods are not available.
ABC methods originated in population and evolutionary genetics [1][2] but have recently also been introduced to the analysis of complex and stochastic dynamical systems [3]. They can be combined with the standard computational approaches used in Bayesian inference such as Markov chain Monte Carlo [4] [5] and Sequential Monte Carlo method [3]approaches. An increasing number of software implementations of ABC approaches exist [6].
See also
References
- ^ Pritchard, J. K. (1999). "Population Growth of Human Y Chromosomes: A Study of Y Chromosome Microsatellites". Mol. Biol. Evol. 16: 1791–1798.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ Beaumont, M. A. (Dec 2002). "Approximate Bayesian Computation in Population Genetics". Genetics. 162: 2025–2035. PMID 12524368.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ a b Toni, T.; Welch, D.; Strelkowa, N.; Ipsen, A.; Stumpf, M.P.H. (2009). "Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems". Journal of the Royal Society Interface. 6 (31): 187–202.
{{cite journal}}: Text "doi: 10.1098/rsif.2008.0172" ignored (help)CS1 maint: multiple names: authors list (link) - ^ Marjoram, P. (2003). "Markov chain Monte Carlo without likelihoods". P Natl Acad Sci USA. 100 (26): 15324–15328. doi:10.1073/pnas.0306899100. PMID 14663152.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) - ^ Plagnol, V. (2004). "Approximate Bayesian computation and MCMC" (PDF). Monte Carlo and Quasi-Monte Carlo Methods 2002.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help) (The link is to a preprint.) - ^ Cornuet, J-M. (2008). "Inferring population history with DIY ABC: a user-friendly approach to Approximate Bayesian Computation". Bioinformatics. doi:10.1093/bioinformatics/btn514. PMID 18842597.
{{cite journal}}: Unknown parameter|coauthors=ignored (|author=suggested) (help)
Softwares
DIYABC : "Do it yourself ABC".
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