পদ্ধতির তুলনা করুন

	বেয়েশীয় এজেন্ট-ভিত্তিক মডেলিং	অ্যাপ্রক্সিমেট বেয়েশিয়ান কম্পিউটেশন
ক্ষেত্র	অনুকরণ	অনুকরণ
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2000s–2010s	2002
প্রবর্তক	Sunnaker et al. / Grazzini & Richiardi (among key contributors)	—
ধরন	Simulation calibration and inference framework	Simulation-based Bayesian inference
মৌলিক উৎস	Sunnaker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., Dessimoz, C. (2013). Approximate Bayesian Computation. PLOS Computational Biology, 9(1), e1002803.	Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.
অপর নাম	Bayesian ABM, ABC-ABM, Bayesian Calibration of ABM, Bayesian Agent Simulation	ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC)
সম্পর্কিত	5	5
সারসংক্ষেপ	Bayesian Agent-Based Modeling integrates Bayesian statistical inference with agent-based simulation to calibrate model parameters and quantify uncertainty. Rather than fixing agent rules and parameters by assumption, this approach treats unknown parameters as probability distributions and updates them systematically against observed data, yielding a full posterior over plausible model configurations.	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.