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| Προσέγγιση Μπεϋζιανής Στατιστικής με Σφάλμα Μέτρησης× | Bayesian Inference with Measurement Error× | |
|---|---|---|
| Πεδίο | Μπεϋζιανή Στατιστική | Μπεϋζιανή Στατιστική |
| Οικογένεια | Bayesian methods | Bayesian methods |
| Έτος προέλευσης≠ | 2013 (measurement-error extension); ABC: 1997-2002 | 1993 |
| Δημιουργός≠ | Wilkinson, R. D. (formal treatment); ABC roots: Tavaré, Diggle, Beaumont et al. (1997-2002) | Richardson & Gilks (Bayesian formulation); Carroll et al. (comprehensive framework) |
| Τύπος≠ | likelihood-free Bayesian inference | Bayesian errors-in-variables model |
| Θεμελιώδης πηγή≠ | Wilkinson, R. D. (2013). Approximate Bayesian computation (ABC) gives exact results under the assumption of model error. Statistical Applications in Genetics and Molecular Biology, 12(2), 129-141. DOI ↗ | 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 |
| Εναλλακτικές ονομασίες | ABC with measurement error, ABC-ME, likelihood-free inference with measurement error, simulation-based inference under measurement error | Bayesian errors-in-variables model, Bayesian EIV model, Bayesian measurement error model, Bayesian misclassification model |
| Συναφείς | 5 | 5 |
| Σύνοψη≠ | Approximate Bayesian Computation with measurement error (ABC-ME) extends the standard ABC likelihood-free framework to settings where observed data are themselves noisy or imprecisely recorded. By explicitly incorporating a measurement-error kernel into the acceptance step, ABC-ME targets the correct posterior over model parameters even when the true data-generating process cannot be directly observed. | 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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