ML Anthology

Statistical Physics, Mixtures of Distributions, and the EM Algorithm

Alan L. Yuille, Paul E. Stolorz, Joachim Utans
NeCo 1994 pp. 334-340
doi:10.1162/NECO.1994.6.2.334 /neco/1994/yuille1994neco-statistical-a/

Abstract

We show that there are strong relationships between approaches to optmization and learning based on statistical physics or mixtures of experts. In particular, the EM algorithm can be interpreted as converging either to a local maximum of the mixtures model or to a saddle point solution to the statistical physics system. An advantage of the statistical physics approach is that it naturally gives rise to a heuristic continuation method, deterministic annealing, for finding good solutions.

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Text

Yuille et al. "Statistical Physics, Mixtures of Distributions, and the EM Algorithm." Neural Computation, 1994. doi:10.1162/NECO.1994.6.2.334

Markdown

[Yuille et al. "Statistical Physics, Mixtures of Distributions, and the EM Algorithm." Neural Computation, 1994.](https://mlanthology.org/neco/1994/yuille1994neco-statistical-a/) doi:10.1162/NECO.1994.6.2.334

BibTeX

@article{yuille1994neco-statistical-a,
  title     = {{Statistical Physics, Mixtures of Distributions, and the EM Algorithm}},
  author    = {Yuille, Alan L. and Stolorz, Paul E. and Utans, Joachim},
  journal   = {Neural Computation},
  year      = {1994},
  pages     = {334-340},
  doi       = {10.1162/NECO.1994.6.2.334},
  volume    = {6},
  url       = {https://mlanthology.org/neco/1994/yuille1994neco-statistical-a/}
}