VB Home VB Papers VB Software VB People Submit

Variational-Bayes .org


Welcome to Variational-Bayes.org. This is a repository of papers, software, and links related to the use of variational methods for approximate Bayesian learning.


    Variational Bayesian (VB) methods, also called "ensemble learning", are a family of techniques for approximating intractable integrals arising in Bayesian statistics and machine learning. They are an alternative to other approaches for approximate Bayesian inference such as Markov chain Monte Carlo, the Laplace approximation, etc. They can be used to lower bound the marginal likelihood (i.e. "evidence") of several models with a view to performing model selection, and often provide an analytical approximation to the parameter posterior which is useful for prediction.

    NOTE: Although there are a variety of variational methods that can be used to approximate Bayesian integrals, in this repository we will focus on methods that provide bounds on these integrals (e.g. by using Jensen's inequality).


    We hope that this resource will be useful to keep researchers and practitioners in statistics and machine learning abreast of this field. Having a single repository for papers and software in this area will make it easy to keep track of and build on the work of others.

Please help:

    We would kindly ask you to keep us informed of new papers, software, suggestions, and older papers that were missed on our first pass.

    We plan to include:

    • all relevant papers that use variational methods to approximate Bayesian integrals (i.e. integrals over model parameters and latent variables),
    • theoretical papers that analyse variational Bayesian methods, and
    • papers with novel applications of VB methods.

    Simply follow the submit link to contact either of us.

Maintained by: