Variational Bayes Inference System

An Variational Bayes Inference System is a Stochastic Approximate Bayesian Inference System that implements an variational Bayes inference algorithm to solve an variational Bayes inference task.

• • …
• Counter-Example(s):
  • a MCMC System.
• See: BayesPy, Bayesian Inference System, Variational Bayes Inference, Collapsed Variational Inference.

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References

2017

• https://github.com/bayespy/bayespy
  • QUOTE: BayesPy provides tools for Bayesian inference with Python. The user constructs a model as a Bayesian network, observes data and runs posterior inference. The goal is to provide a tool which is efficient, flexible and extendable enough for expert use but also accessible for more casual users.

    Currently, only variational Bayesian inference for conjugate-exponential family (variational message passing) has been implemented. Future work includes variational approximations for other types of distributions and possibly other approximate inference methods such as expectation propagation, Laplace approximations, Markov chain Monte Carlo (MCMC) and other methods. Contributions are welcome.

2014

• Jaakko Luttinen. (2014). “BayesPy: Variational Bayesian Inference in Python."
  • QUOTE: BayesPy is an open-source Python software package for performing variational Bayesian inference. It is based on the variational message passing framework and supports conjugate exponential family models. By removing the tedious task of implementing the variational Bayesian update equations, the user can construct models faster and in a less error-prone way. Simple syntax, flexible model construction and efficient inference make BayesPy suitable for both average and expert Bayesian users. It also supports some advanced methods such as stochastic and collapsed variational inference.

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