Ormerod, J. T.; Wand, M. P. Explaining variational approximations. (English) Zbl 1200.65007 Am. Stat. 64, No. 2, 140-153 (2010). Summary: 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. Cited in 83 Documents MSC: 65C60 Computational problems in statistics (MSC2010) 62F15 Bayesian inference 65C05 Monte Carlo methods Keywords:Bayesian inference; Bayesian networks; directed acyclic graphs; generalized linear mixed models; Kullback-Leibler divergence; linear mixed models; variational approximations; complex statistical models; Monte Carlo methods PDFBibTeX XMLCite \textit{J. T. Ormerod} and \textit{M. P. Wand}, Am. Stat. 64, No. 2, 140--153 (2010; Zbl 1200.65007) Full Text: DOI