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| Bejzijevska mreža sa greškom merenja× | Bejzovsko zaključivanje uz grešku merenja× | |
|---|---|---|
| Oblast | Bajesovska statistika | Bajesovska statistika |
| Porodica | Bayesian methods | Bayesian methods |
| Godina nastanka≠ | 1988 (Bayesian networks); measurement-error extension: 1990s | 1993 |
| Tvorac≠ | 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) |
| Tip≠ | Probabilistic graphical model with latent variables | Bayesian errors-in-variables model |
| Temeljni izvor≠ | 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 |
| Drugi nazivi | 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 |
| Srodne | 5 | 5 |
| Sažetak≠ | 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. |
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