Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
Abstract We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals to a new class of Pólya–Gamma distributions, which are constructed in detail. A variety of examples are presented to show the versatility of the method, including logistic regression, negative binomial regression, …