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| Inferens Bayesian dengan Ralat Pengukuran× | Inferensi Bayesian Hierarki× | |
|---|---|---|
| Bidang | Bayesian | Bayesian |
| Keluarga | Bayesian methods | Bayesian methods |
| Tahun asal≠ | 1993 | 1972 (Lindley & Smith); consolidated 1995–2013 |
| Pengasas≠ | Richardson & Gilks (Bayesian formulation); Carroll et al. (comprehensive framework) | Lindley & Smith; Gelman et al. |
| Jenis≠ | Bayesian errors-in-variables model | Bayesian multilevel model |
| Sumber perintis≠ | 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 | 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 |
| Alias | Bayesian errors-in-variables model, Bayesian EIV model, Bayesian measurement error model, Bayesian misclassification model | multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model |
| Berkaitan≠ | 5 | 6 |
| Ringkasan≠ | 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. | Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate. |
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