Bayesian Multinomial Logistic Regression
Bayesian Multinomial Logistic Regression models a nominal outcome with three or more unordered categories by placing prior distributions over the regression coefficients and updating them with data via Bayes' theorem. The result is a full posterior distribution over category probabilities for each observation, enabling principled uncertainty quantification and regularization through the prior.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- 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
- Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. · ISBN 978-0471360933
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