विधियों की तुलना करें

	बेयसियन नेटवर्क	बेयसियन रिग्रेशन	DAG Causal Identification	मार्कोव चेन मोंटे कार्लो (MCMC)
क्षेत्र	बायेसियन	बायेसियन	कारणात्मक अनुमान	बायेसियन
परिवार	Bayesian methods	Bayesian methods	Regression model	Bayesian methods
उद्भव वर्ष	1988	—	2009	—
प्रवर्तक	Judea Pearl	—	Judea Pearl	—
प्रकार	Probabilistic graphical model	Bayesian linear model	Causal identification framework	Posterior sampling algorithm
मौलिक स्रोत	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	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
उपनाम	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)	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
संबंधित	4	2	5	3
सारांश	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.	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.