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	Sieć bayesowska	Łańcuchy Markowa i symulacje Monte Carlo (MCMC)	Modelowanie równań strukturalnych
Dziedzina	Statystyka bayesowska	Statystyka bayesowska	Statystyka w badaniach
Rodzina	Bayesian methods	Bayesian methods	Process / pipeline
Rok powstania	1988	—	1921
Twórca	Judea Pearl	—	Sewall Wright
Typ	Probabilistic graphical model	Posterior sampling algorithm	Method
Źródło pierwotne	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	Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.
Inne nazwy	Bayes network, belief network, probabilistic graphical model, directed graphical model	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)	SEM, path analysis, latent variable modeling, causal modeling
Pokrewne	4	3	3
Podsumowanie	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.	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.	Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.