Review paper

Variational Inference: A Review for Statisticians

Volume: 112, Issue: 518, Pages: 859 - 877
Published: Feb 27, 2017
Abstract
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 variational inference (VI), a method from machine learning that approximates probability densities through optimization. VI has been used in many...
Paper Details
Title
Variational Inference: A Review for Statisticians
Published Date
Feb 27, 2017
Volume
112
Issue
518
Pages
859 - 877
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