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	Przybliżone Obliczenia Bayesa	Wnioskowanie bayesowskie
Dziedzina	Symulacja	Statystyka
Rodzina	Process / pipeline	Bayesian methods
Rok powstania	2002	1763
Twórca	—	Thomas Bayes; Pierre-Simon Laplace
Typ	Simulation-based Bayesian inference	Probabilistic inference paradigm
Źródło pierwotne	Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.	Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.
Inne nazwy	ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC)	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
Pokrewne	5	3
Podsumowanie	Approximate Bayesian Computation (ABC) is a family of simulation-based inference methods that estimate posterior distributions without requiring an analytically tractable likelihood function. Introduced by Beaumont, Zhang and Balding (2002) in the context of population genetics, ABC replaced the intractable likelihood with repeated model simulation and a comparison of summary statistics between simulated and observed data.	Bayesian inference is a statistical paradigm in which probability represents degrees of belief rather than long-run frequencies. It encodes prior knowledge about parameters in a prior distribution, combines that prior with the likelihood of observed data via Bayes' theorem, and produces a posterior distribution that quantifies updated uncertainty. The foundational theorem was published posthumously by Thomas Bayes in 1763 and subsequently systematized by Pierre-Simon Laplace in his 1812 Théorie analytique des probabilités.