Grid based variational approximations
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.
Year of publication: |
2011
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Authors: | Ormerod, John T. |
Published in: |
Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 55.2011, 1, p. 45-56
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Publisher: |
Elsevier |
Keywords: | Bayesian Inference Variational approximation Kullback-Liebler divergence Markov chain Monte Carlo |
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