Ensemble learning for multi-layer networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An Introduction to Variational Methods for Graphical Models
Machine Learning
Bayesian parameter estimation via variational methods
Statistics and Computing
WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility
Statistics and Computing
Formulas for Rényi information and related measures for univariate distributions
Information Sciences: an International Journal
The Journal of Machine Learning Research
Mean-field variational approximate Bayesian inference for latent variable models
Computational Statistics & Data Analysis
The variational gaussian approximation revisited
Neural Computation
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Variational methods for approximate Bayesian inference provide fast, flexible, deterministic alternatives to Monte Carlo methods. Unfortunately, unlike Monte Carlo methods, variational approximations cannot, in general, be made to be arbitrarily accurate. This paper develops grid-based variational approximations which endeavor to approximate marginal posterior densities in a spirit similar to the Integrated Nested Laplace Approximation (INLA) of Rue et al. (2009) but which may be applied in situations where INLA cannot be used. The method can greatly increase the accuracy of a base variational approximation, although not in general to arbitrary accuracy. The methodology developed is at least reasonably accurate on all of the examples considered in the paper.