A1087
Title: Bayesian generalized linear models for correlated data with fewer latent variables
Authors: Maryclare Griffin - University of Massachusetts Amherst (United States) [presenting]
Abstract: Many challenges arise when simulating Bayesian generalized linear model posterior distributions in practice, especially when the observed data is assumed to be dependent. The focus is on two challenges that stem from the introduction of one or more auxiliary latent variables for each observation. First, several popular methods for simulating from Bayesian generalized linear model posterior distributions rely on the introduction of an auxiliary random variable for each observation. These methods can scale poorly when the number of observations is large because they require additional posterior draws and repeated expensive matrix calculations. Second, many of the most useful approaches for introducing dependence in the observed data do so by introducing a latent random variable with a dense but computationally convenient prior covariance matrix. However, the computational conveniences offered by the prior covariance matrix may be absent (or appear to be absent) from the posterior. Methods are introduced to address these challenges that take advantage of simple reparameterizations of the problem, advances in posterior mode computation, and modern sampling.
