Approximate Bayesian computation


Approximate Bayesian computation

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.

ee also

Markov chain Monte Carlo

References

*cite journal|last = Pritchard|first = J. K.|coauthors = Seielstad, M. T., Perez-Lezaun, A., and Feldman, M. T.|title = Population Growth of Human Y Chromosomes: A Study of Y Chromosome Microsatellites|journal = Mol. Biol. Evol.|volume = 16|date = 1999|pages = 1791–1798
*cite journal|last = Beaumont|first = M. A.|coauthors = Zhang, W. and Balding, D. J.|title = Approximate Bayesian Computation in Population Genetics|journal = Genetics|volume = 162|date = Dec 2002|pages = 2025–2035|url = http://www.genetics.org/cgi/content/abstract/162/4/2025|pmid = 12524368
*cite journal|last = Marjoram|first = P.|coauthors = Molitor, J., Plagnol, V. and Tavaré, S.|title = Markov chain Monte Carlo without likelihoods|journal = P Natl Acad Sci USA|volume = 100|number = 26|date = 2003|pages = 15324–15328|doi = 10.1073/pnas.0306899100|pmid = 14663152
*cite journal|last = Plagnol|first = V.|coauthors = Tavaré, S.|title = Approximate Bayesian computation and MCMC|journal = Monte Carlo and Quasi-Monte Carlo Methods 2002|year = 2004|url = http://www-gene.cimr.cam.ac.uk/vplagnol/papers/vpst-web.pdf (The link is to a preprint.)


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