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
References
- Pritchard, J. K.; Seielstad, M. T., Perez-Lezaun, A., and Feldman, M. T. (1999). "Population Growth of Human Y Chromosomes: A Study of Y Chromosome Microsatellites". Mol. Biol. Evol. 16: 1791-1798.
- Beaumont, M. A.; Zhang, W. and Balding, D. J. (Dec 2002). "Approximate Bayesian Computation in Population Genetics". Genetics 162: 2025-2035.
- Marjoram, P.; Molitor, J., Plagnol, V. and Tavaré, S. (2003). "Markov chain Monte Carlo without likelihoods". P Natl Acad Sci USA 100: 15324-15328.
- Plagnol, V.; Tavaré, S. (2004). "Approximate Bayesian computation and MCMC". Monte Carlo and Quasi-Monte Carlo Methods 2002. (The link is to a preprint.)
