Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation
Abstract
Bayesian learning has been widely used and proved to be effective in many data modeling problems. However, computations involved in it require huge costs and generally cannot be performed exactly. The variational Bayesian approach, proposed as an approximation of Bayesian learning, has provided computational tractability and good generalization performance in many applications.
Cite
Text
Watanabe and Watanabe. "Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation." Journal of Machine Learning Research, 2006.Markdown
[Watanabe and Watanabe. "Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation." Journal of Machine Learning Research, 2006.](https://mlanthology.org/jmlr/2006/watanabe2006jmlr-stochastic/)BibTeX
@article{watanabe2006jmlr-stochastic,
title = {{Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation}},
author = {Watanabe, Kazuho and Watanabe, Sumio},
journal = {Journal of Machine Learning Research},
year = {2006},
pages = {625-644},
volume = {7},
url = {https://mlanthology.org/jmlr/2006/watanabe2006jmlr-stochastic/}
}