Bayesian Inference for Logistic Models Using PÃ³lya--Gamma Latent Variables
Nicholas G. Polson,
James G. Scott and
Journal of the American Statistical Association, 2013, vol. 108, issue 504, 1339-1349
We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals to a new class of PÃ³lya--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 PÃ³lya--Gamma distribution, are implemented in the R package BayesLogit . Supplementary materials for this article are available online.
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