# variational Bayes EM

## Summary

Variational Bayes EM is the application of variational Bayes to latent variable models. In the approximating distribution, the latent variables and parameters are independent, and often there are additional variational approximations within either the latent variables or the parameters.

## Context

This concept has the prerequisites:

## Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)

## Supplemental resources (the following are optional, but you may find them useful)

## -Paid-

→ Pattern Recognition and Machine Learning

A textbook for a graduate machine learning course, with a focus on Bayesian methods.

Location:
Section 10.2, pages 474-486

Additional dependencies:

- mixture of Gaussians models

→ Machine Learning: a Probabilistic Perspective

A very comprehensive graudate-level machine learning textbook.

Location:
Section 21.6, pages 749-756

Additional dependencies:

- mixture of Gaussians models

## See also

-No Additional Notes-