Bayesian inference for logistic models using Pólya-Gamma latent variables. 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, nonlinear mixed-effect models, and spatial models for count data. In each case, our data-augmentation strategy leads to simple, effective methods for posterior inference that (1) circumvent the need for analytic approximations, numerical integration, or Metropolis-Hastings; and (2) outperform other known data-augmentation strategies, both in ease of use and in computational efficiency. All methods, including an efficient sampler for the Pólya-Gamma distribution, are implemented in the R package BayesLogit. Supplementary materials for this article are available online.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
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- Choi, Hee Min; Hobert, James P.: The Pólya-Gamma Gibbs sampler for Bayesian logistic regression is uniformly ergodic (2013)
- Polson, Nicholas G.; Scott, James G.; Windle, Jesse: Bayesian inference for logistic models using Pólya-Gamma latent variables (2013)