ML Anthology

Bayesian Analysis of Mixtures of Factor Analyzers

Akio Utsugi, Toru Kumagai
NeCo 2001 pp. 993-1002
doi:10.1162/08997660151134299 /neco/2001/utsugi2001neco-bayesian/

Abstract

For Bayesian inference on the mixture of factor analyzers, natural conjugate priors on the parameters are introduced, and then a Gibbs sampler that generates parameter samples following the posterior is constructed. In addition, a deterministic estimation algorithm is derived by taking modes instead of samples from the conditional posteriors used in the Gibbs sampler. This is regarded as a maximum a posteriori estimation algorithm with hyperparameter search. The behaviors of the Gibbs sampler and the deterministic algorithm are compared on a simulation experiment.

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Text

Utsugi and Kumagai. "Bayesian Analysis of Mixtures of Factor Analyzers." Neural Computation, 2001. doi:10.1162/08997660151134299

Markdown

[Utsugi and Kumagai. "Bayesian Analysis of Mixtures of Factor Analyzers." Neural Computation, 2001.](https://mlanthology.org/neco/2001/utsugi2001neco-bayesian/) doi:10.1162/08997660151134299

BibTeX

@article{utsugi2001neco-bayesian,
  title     = {{Bayesian Analysis of Mixtures of Factor Analyzers}},
  author    = {Utsugi, Akio and Kumagai, Toru},
  journal   = {Neural Computation},
  year      = {2001},
  pages     = {993-1002},
  doi       = {10.1162/08997660151134299},
  volume    = {13},
  url       = {https://mlanthology.org/neco/2001/utsugi2001neco-bayesian/}
}