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A practical tutorial on Variational Bayes
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This tutorial gives a quick introduction to Variational Bayes (VB), also called Variational Inference or Variational Approximation, from a practical point of view. The paper covers a range of commonly used VB methods and an attempt is made to keep the materials accessible to the wide community of data analysis practitioners. The aim is that the reader can quickly derive and implement their first VB algorithm for Bayesian inference with their data analysis problem. An end-user software package in Matlab together with the documentation can be found at https://vbayeslab.github.io/VBLabDocs/
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Cited by 1 Pith paper
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Variational Bayes on Manifolds
A manifold-based variational Bayes algorithm with natural-gradient updates is shown to converge at O(1/sqrt(T)) for non-convex ELBOs and faster under retraction-convexity, with Gaussian and Wishart implementations.
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