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.

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. PMID 12524368. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
• Marjoram, P. (2003). "Markov chain Monte Carlo without likelihoods". P Natl Acad Sci USA. 100 (26): 15324–15328. doi:10.1073/pnas.0306899100. {{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.)