Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Bayesian Instrumental Variables (Bayesian IV)× | Usajili wa Bayesian× | |
|---|---|---|
| Nyanja≠ | Uhitimisho wa Kisababishi | Mbinu za Bayes |
| Familia≠ | Regression model | Bayesian methods |
| Mwaka wa asili≠ | 2003 | — |
| Mwanzilishi≠ | Kleibergen & Zivot (2003); Lancaster (2004) | — |
| Aina≠ | Causal inference / Bayesian estimation | Bayesian linear model |
| Chanzo asilia≠ | Kleibergen, F., & Zivot, E. (2003). Bayesian and classical approaches to instrumental variable regression. Journal of Econometrics, 114(1), 29-72. DOI ↗ | 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 |
| Majina mbadala≠ | Bayesian IV, Bayesian 2SLS, Bayesian LIML, BayesIV | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Zinazohusiana≠ | 6 | 2 |
| Muhtasari≠ | Bayesian Instrumental Variables combines the instrumental variable strategy for addressing endogeneity with Bayesian posterior inference. Instead of relying on asymptotic sampling distributions, it places prior distributions over all structural parameters and recovers a full posterior distribution for the causal effect, providing probability statements about the parameter rather than p-values — especially valuable when instruments are weak or the sample is small. | 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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