Variational Bayes Made Easy
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Abstract (arXiv)
Variational Bayes is a popular method for approximate inference but its derivation can be cumbersome. To simplify the process, we give a 3-step recipe to identify the posterior form by explicitly looking for linearity with respect to expectations of well-known distributions. We can then directly write the update by simply ``reading-off'' the terms in front of those expectations. The recipe makes the derivation easier, faster, shorter, and more general.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 20 references that are in the map. First posted on arXiv 27 Apr 2023.
Builds on
8 of 20 references.
References · 8
- 2016Variational Inference: A Review for Statisticianscited by 1,472
- 2021The Bayesian Learning Rulecited by 99
- 2017Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Modelscited by 65
- 2019Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximationscited by 51
- 2021Tractable structured natural gradient descent using local parameterizationscited by 22
- 2015Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functionscited by 19
- 2023The Lie-Group Bayesian Learning Rulecited by 3
- 2015On the Convergence of Stochastic Variational Inference in Bayesian Networkscited by 1
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Citing papers per year: 2023: 1.
1 paper.