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2023

A Tutorial on Parametric Variational Inference

Sjölund, Jens

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Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model.

Thanks to advances in computational scalability made in the last decade, variational inference is now the preferred choice for many high-dimensional models and large datasets.
This tutorial introduces variational inference from the parametric perspective that dominates these recent developments, in contrast to the mean-field perspective commonly found in other introductory texts.

A Tutorial on Parametric Variational Inference · Around

Built on

“Simple statistical gradient-following algorithms for connectionist reinforcement learning”
Ronald Williams · 1992
Earlier work this paper cites.
“Random variate generation in one line of code”
Luc Devroye · 1996
Earlier work this paper cites.
“Graphical models, exponential families, and variational inference”
Martin Wainwright and Michael Jordan · 2008
Earlier work this paper cites.
“Auto-Encoding Variational Bayes”
Diederik Kingma and Max Welling · 2014
Earlier work this paper cites.

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Then

“Variational inference: A review for statisticians”
David Blei, Alp Kucukelbir and Jon McAuliffe · 2017
Later among the works it cites.
“An introduction to variational autoencoders”
Diederik Kingma and Max Welling · 2019
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“Tutorial on amortized optimization for learning to optimize over continuous domains”
Brandon Amos · 2022
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