So sánh phương pháp

	Mạng Bayes	Hồi quy Bayes	Nhận dạng nhân quả với Đồ thị có hướng không chu trình (do-calculus)
Lĩnh vực	Bayes	Bayes	Suy luận nhân quả
Họ	Bayesian methods	Bayesian methods	Regression model
Năm ra đời	1988	—	2009
Người khởi xướng	Judea Pearl	—	Judea Pearl
Loại	Probabilistic graphical model	Bayesian linear model	Causal identification framework
Công trình gốc	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	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
Tên gọi khác	Bayes network, belief network, probabilistic graphical model, directed graphical model	bayesian linear regression, probabilistic regression, bayesian regresyon	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)
Liên quan	4	2	5
Tóm tắt	A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others.	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.