Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression

30 Dec 2016Quan ZhangMingyuan Zhou

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... (read more)

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