方法证据记录
Bayesian Ordinal Logistic Regression
Bayesian ordinal logistic regression extends the classical proportional odds model by placing prior distributions on the regression coefficients and threshold parameters and updating them with observed data via Bayes' theorem. The result is a full posterior distribution over all parameters, enabling uncertainty quantification without relying on large-sample approximations.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Bayesian Ordinal Logistic Regression (Proportional Odds Model)
分类方法记录 · regression-model / statistics
- Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. · ISBN 978-0387987484
- 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
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