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| MCMC z błędem pomiaru× | Regresja bayesowska× | |
|---|---|---|
| Dziedzina | Statystyka bayesowska | Statystyka bayesowska |
| Rodzina | Bayesian methods | Bayesian methods |
| Rok powstania≠ | 1993 | — |
| Twórca≠ | Richardson & Gilks; Carroll, Ruppert & Stefanski | — |
| Typ≠ | Bayesian computational estimation | Bayesian linear model |
| Źródło pierwotne≠ | 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: 978-1584886334 | 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 |
| Inne nazwy≠ | MCMC errors-in-variables, Bayesian measurement error MCMC, MCMC misclassification model, Bayesian errors-in-variables | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Pokrewne≠ | 6 | 2 |
| Podsumowanie≠ | MCMC with measurement error applies Markov chain Monte Carlo sampling to Bayesian models that explicitly account for the fact that covariates or outcomes are observed with error. By treating the true, unobserved values as latent variables and sampling their joint posterior alongside all other parameters, the method corrects for attenuation bias and produces valid inference even when some variables cannot be measured exactly. | 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. |
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