Variational Inference: A Review for Statisticians

One of the core problems of modern statistics is to approximate difficult-to-compute probability densities. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation involving the posterior density. In this article, we review va...

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Veröffentlicht in:	Journal of the American Statistical Association Jg. 112; H. 518; S. 859 - 877
Hauptverfasser:	Blei, David M., Kucukelbir, Alp, McAuliffe, Jon D.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Alexandria Taylor & Francis 03.04.2017 Taylor & Francis Group,LLC Taylor & Francis Ltd
Schlagworte:	Algorithms Americans artificial intelligence Bayesian analysis Bayesian theory catalytic activity Closeness Computationally intensive methods Density Families & family life Inference Machine learning Markov analysis Markov chain Markov chains mixtures Optimization Probability probability distribution Review Sampling Scholarship Statistical analysis Statistical computing Statistical inference Statistics
ISSN:	0162-1459, 1537-274X, 1537-274X
Online-Zugang:	Volltext
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