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==External links==
* [http://www.inference.phy.cam.ac.uk/mackay/itila/ The on-line textbook: Information Theory, Inference, and Learning Algorithms], by [[David J.C. MacKay]] provides an introduction to variational methods (p. 422).
* [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4541076 An in depth introduction to Variational Bayes note]. Nguyen, D. 2023▼
* [http://www.robots.ox.ac.uk/~sjrob/Pubs/fox_vbtut.pdf A Tutorial on Variational Bayes]. Fox, C. and Roberts, S. 2012. Artificial Intelligence Review, {{doi|10.1007/s10462-011-9236-8}}.
* [http://www.gatsby.ucl.ac.uk/vbayes/ Variational-Bayes Repository] A repository of research papers, software, and links related to the use of variational methods for approximate Bayesian learning up to 2003.
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* [http://www.cs.jhu.edu/~jason/tutorials/variational.html High-Level Explanation of Variational Inference] by Jason Eisner may be worth reading before a more mathematically detailed treatment.
* [https://arxiv.org/abs/1803.10998 Copula Variational Bayes inference via information geometry (pdf)] by Tran, V.H. 2018. This paper is primarily written for students. Via [[Bregman divergence]], the paper shows that Variational Bayes is simply a generalized Pythagorean projection of true model onto an arbitrarily correlated (copula) distributional space, of which the independent space is merely a special case.
▲* [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4541076 An in depth introduction to Variational Bayes note]. Nguyen, D. 2023
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