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Approximate Bayesian computation

This is an old revision of this page, as edited by DavidCBryant (talk | contribs) at 22:44, 21 June 2007 (Added references, plus a wiki-link to a related article. Summarized the method based on my reading of the abstract of Beaumont's article.). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

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.

See also

Markov chain Monte Carlo

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. {{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.)
 

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