Linganisha mbinu

	Usajili wa Bayesian	Uchanganuzi wa Kimuundo wa Milongozo (SEM)
Nyanja	Mbinu za Bayes	Takwimu za Utafiti
Familia	Bayesian methods	Process / pipeline
Mwaka wa asili	—	1921
Mwanzilishi	—	Sewall Wright
Aina	Bayesian linear model	Method
Chanzo asilia	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.
Majina mbadala	bayesian linear regression, probabilistic regression, bayesian regresyon	SEM, path analysis, latent variable modeling, causal modeling
Zinazohusiana	2	3
Muhtasari	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.	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.