Vertaile menetelmiä

	Bayesiläinen verkko mittausvirheellä	Bayesiläinen päättely mittausvirheellä
Tieteenala	Bayesilainen tilastotiede	Bayesilainen tilastotiede
Menetelmäperhe	Bayesian methods	Bayesian methods
Syntyvuosi	1988 (Bayesian networks); measurement-error extension: 1990s	1993
Kehittäjä	Judea Pearl (Bayesian networks); measurement-error extension developed in epidemiology and psychometrics through the 1990s–2000s	Richardson & Gilks (Bayesian formulation); Carroll et al. (comprehensive framework)
Tyyppi	Probabilistic graphical model with latent variables	Bayesian errors-in-variables model
Alkuperäislähde	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Chapman & Hall/CRC. ISBN: 978-1584886433
Rinnakkaisnimet	BN-ME, errors-in-variables Bayesian network, Bayesian graphical model with measurement error, latent variable Bayesian network	Bayesian errors-in-variables model, Bayesian EIV model, Bayesian measurement error model, Bayesian misclassification model
Liittyvät	5	5
Tiivistelmä	A Bayesian network with measurement error is a probabilistic directed acyclic graphical model in which one or more node variables are observed with error rather than exactly. Latent true-value nodes are introduced for mismeasured variables, and the model jointly infers the network's conditional probability parameters and the unobserved true values from the noisy observations.	Bayesian inference with measurement error extends the standard Bayesian framework to situations where one or more covariates or outcomes are observed with noise or misclassification. By treating the true unobserved values as latent variables and assigning them priors, the model jointly estimates the true exposure distribution and the structural parameters of interest, propagating all uncertainty through the posterior.