विधियों की तुलना करें

	मॉडल तुलना के लिए MCMC	सन्निकट बेयसियन अभिकलन
क्षेत्र	बायेसियन	अनुकरण
परिवार	Bayesian methods	Process / pipeline
उद्भव वर्ष	1995	2002
प्रवर्तक	Peter J. Green (reversible-jump MCMC); Meng & Wong (bridge sampling)	—
प्रकार	Bayesian computational method	Simulation-based Bayesian inference
मौलिक स्रोत	Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711–732.	Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.
उपनाम	reversible-jump MCMC, RJMCMC, marginal likelihood estimation via MCMC, Bayesian model selection via MCMC	ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC)
संबंधित	5	5
सारांश	MCMC for model comparison uses Markov chain Monte Carlo algorithms to estimate the marginal likelihoods and Bayes factors needed to formally compare competing statistical models. Techniques such as reversible-jump MCMC and bridge sampling allow exploration across model spaces of different dimensionality, enabling fully Bayesian model selection and averaging.	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.