ML Anthology

Variational EM Algorithms for Non-Gaussian Latent Variable Models

Jason Palmer, Kenneth Kreutz-Delgado, Bhaskar D. Rao, David P. Wipf
NeurIPS 2005 pp. 1059-1066
/neurips/2005/palmer2005neurips-variational/

Abstract

We consider criteria for variational representations of non-Gaussian latent variables, and derive variational EM algorithms in general form. We establish a general equivalence among convex bounding methods, evidence based methods, and ensemble learning/Variational Bayes methods, which has previously been demonstrated only for particular cases.

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Cite

Text

Palmer et al. "Variational EM Algorithms for Non-Gaussian Latent Variable Models." Neural Information Processing Systems, 2005.

Markdown

[Palmer et al. "Variational EM Algorithms for Non-Gaussian Latent Variable Models." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/palmer2005neurips-variational/)

BibTeX

@inproceedings{palmer2005neurips-variational,
  title     = {{Variational EM Algorithms for Non-Gaussian Latent Variable Models}},
  author    = {Palmer, Jason and Kreutz-Delgado, Kenneth and Rao, Bhaskar D. and Wipf, David P.},
  booktitle = {Neural Information Processing Systems},
  year      = {2005},
  pages     = {1059-1066},
  url       = {https://mlanthology.org/neurips/2005/palmer2005neurips-variational/}
}