Journal
AMERICAN STATISTICIAN
Volume 64, Issue 2, Pages 140-153Publisher
TAYLOR & FRANCIS INC
DOI: 10.1198/tast.2010.09058
Keywords
Bayesian inference; Bayesian networks; Directed acyclic graphs; Generalized linear mixed models; Kullback-Leibler divergence; Linear mixed models
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Funding
- Australian Research Council [DP0877055]
- Australian Research Council [DP0877055] Funding Source: Australian Research Council
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Variational approximations facilitate approximate inference for the parameters in complex statistical models and provide fast, deterministic alternatives to Monte Carlo methods. However, much of the contemporary literature on variational approximations is in Computer Science rather than Statistics, and uses terminology, notation, and examples from the former field. In this article we explain variational approximation in statistical terms. In particular, we illustrate the ideas of variational approximation using examples that are familiar to statisticians.
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