RIS ID
94357
Abstract
We investigate mean field variational approximate Bayesian inference for models that use continuous distributions, Horseshoe. Negative-Exponential-Gamma and Generalized Double Pareto, for sparse signal shrinkage. Our principal finding is that the most natural, and simplest, mean field variational Bayes algorithm can perform quite poorly due to posterior dependence among auxiliary variables. More sophisticated algorithms, based on special functions, are shown to be superior. Continued fraction approximations via Lentz's Algorithm are developed to make the algorithms practical.
Grant Number
ARC/DP110100061
Publication Details
Neville, S. E., Ormerod, J. T. & Wand, M. P. (2014). Mean field variational Bayes for continuous sparse signal shrinkage: Pitfalls and remedies. Electronic Journal of Statistics, 8 (1), 1113-1151.