mlsanityData to 7 Oct 2026

A practical tutorial on Variational Bayes

arXiv 2103.01327 ↗First posted 1 Mar 2021stat.CO stat.ME stat.MLcited by 10

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Abstract (arXiv)

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/

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 14 of its 40 references that are in the map. First posted on arXiv 1 Mar 2021.

Builds on

14 of 40 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Importance Weighted Autoencoderscited by 454
  3. 2014New insights and perspectives on the natural gradient methodcited by 435
  4. 2013Black Box Variational Inferencecited by 317
  5. 2017Concentration of tempered posteriors and of their variational approximationscited by 66
  6. 2017Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Modelscited by 65
  7. 2019Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximationscited by 51
  8. 2017Convergence Rates of Variational Posterior Distributionscited by 45
  9. 2017Gaussian variational approximation with a factor covariance structurecited by 22
  10. 2016Gaussian variational approximation with sparse precision matricescited by 15
  11. 2015Variational Bayes with Intractable Likelihoodcited by 12
  12. 2019Variational Bayes on Manifoldscited by 9
2 more
  1. 2020Recurrent Conditional Heteroskedasticitycited by 5
  2. 2013Copula-type Estimators for Flexible Multivariate Density Modeling using Mixturescited by 1

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 6; 2023: 0; 2024: 3.

10 papers.

  1. 2022Machine Learning in Aerodynamic Shape Optimizationcited by 39
  2. 2022Bayesian Learning for Neural Networks: an algorithmic surveycited by 22
  3. 2022Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Marketscited by 5
  4. 2022Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learningcited by 3
  5. 2021Quantum Speedup of Natural Gradient for Variational Bayescited by 1
  6. 2022Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffscited by 1
  7. 2022Manifold Gaussian Variational Bayes on the Precision Matrixcited by 1
  8. 2024Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Depositioncited by 1
  9. 2024Maximum a Posteriori Inference for Factor Graphs via Benders' Decompositioncited by 1
  10. 2024Hierarchical mixtures of Unigram models for short text clustering: The role of Beta-Liouville priorscited by 0