Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression
This work addresses the need for Bayesian multinomial regression models that provide probability estimates and uncertainty quantification, though it appears incremental as it builds on existing stick-breaking and binary classifier frameworks.
The authors tackled the problem of modeling categorical response variables with covariates by proposing a permuted and augmented stick-breaking construction, which transforms multinomial regression into independent binary regressions, enabling the conversion of binary classifiers into Bayesian multinomial ones with demonstrated attractive properties and performance.
To model categorical response variables given their covariates, we propose a permuted and augmented stick-breaking (paSB) construction that one-to-one maps the observed categories to randomly permuted latent sticks. This new construction transforms multinomial regression into regression analysis of stick-specific binary random variables that are mutually independent given their covariate-dependent stick success probabilities, which are parameterized by the regression coefficients of their corresponding categories. The paSB construction allows transforming an arbitrary cross-entropy-loss binary classifier into a Bayesian multinomial one. Specifically, we parameterize the negative logarithms of the stick failure probabilities with a family of covariate-dependent softplus functions to construct nonparametric Bayesian multinomial softplus regression, and transform Bayesian support vector machine (SVM) into Bayesian multinomial SVM. These Bayesian multinomial regression models are not only capable of providing probability estimates, quantifying uncertainty, increasing robustness, and producing nonlinear classification decision boundaries, but also amenable to posterior simulation. Example results demonstrate their attractive properties and performance.