So sánh phương pháp

	Hồi quy Bayes	Nhận dạng nhân quả với Đồ thị có hướng không chu trình (do-calculus)	Chuỗi Markov Monte Carlo (MCMC)
Lĩnh vực	Bayes	Suy luận nhân quả	Bayes
Họ	Bayesian methods	Regression model	Bayesian methods
Năm ra đời	—	2009	—
Người khởi xướng	—	Judea Pearl	—
Loại	Bayesian linear model	Causal identification framework	Posterior sampling algorithm
Công trình gốc	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Tên gọi khác	bayesian linear regression, probabilistic regression, bayesian regresyon	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Liên quan	2	5	3
Tóm tắt	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.