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Activity Number: 585 - Bayesian Neural Networks
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #312674
Title: Bayesian Deep Net GLM and GLMM
Author(s): Robert Kohn and Minh Ngoc Tran*
Companies: University of New South Wales and The University of Sydney
Keywords: Factor model; feed forward neural network ; Natural gradient; Random effects; Variational approximation

We describe ?exible versions of generalized linear and generalized linear mixed models incorporating basis functions formed by a deep feedforward neural networks( DFNN). E?cient computational methods for high dimensional Bayesian inference are developed using Gaussian variational approximation, with a parsimonious but ?exible factor parametrization of the covariance matrix. We implement natural gradient methods for the optimization, exploitingthe factor structure of the variational covariance matrix in computation of the natural gradient. This leads to a regression and classi?cation method having a high predictive accuracy, and that is able to quantify the prediction uncertainty in a principled and convenient way. We also describe how to perform variable selection in our deep learning method. The proposed methods are illustrated in a wide range of simulated and real-data examples, and the results compare favourably to a state of the art ?exible regression and classi?cation method in the statistical literature, the Bayesian additive regression trees (BART) method.

Authors who are presenting talks have a * after their name.

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