পদ্ধতির তুলনা করুন

	অ্যাপ্রক্সিমেট বেয়েশিয়ান কম্পিউটেশন	মার্কভ চেইন মন্টি কার্লো (MCMC)
ক্ষেত্র	অনুকরণ	অনুকরণ
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2002	1953 (Metropolis-Hastings); 1984 (Gibbs)
প্রবর্তক	—	Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984)
ধরন	Simulation-based Bayesian inference	Simulation-based Bayesian inference / numerical integration
মৌলিক উৎস	Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.	Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. & Rubin, D.B. (2013). Bayesian Data Analysis (3rd ed.). Chapman & Hall/CRC.
অপর নাম	ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC)	MCMC, Metropolis-Hastings, Gibbs sampling, Markov Zinciri Monte Carlo (MCMC — Metropolis-Hastings, Gibbs)
সম্পর্কিত	5	5
সারসংক্ষেপ	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.	Markov Chain Monte Carlo (MCMC) is a family of simulation algorithms that constructs a Markov chain whose stationary distribution is the target posterior, enabling Bayesian inference and high-dimensional integral computation that would otherwise be analytically intractable. Pioneered by Metropolis and colleagues in 1953 and extended by Hastings in 1970, MCMC underpins modern Bayesian statistics. The two most widely used variants are Metropolis-Hastings, which proposes moves from a general proposal distribution, and Gibbs sampling, which draws each parameter in turn from its full conditional distribution.