A MCMC Approach to Hierarchical Mixture Modelling

NeurIPS 1999 pp. 680-686

/neurips/1999/williams1999neurips-mcmc/

Abstract

There are many hierarchical clustering algorithms available, but these lack a firm statistical basis. Here we set up a hierarchical probabilistic mixture model, where data is generated in a hierarchical tree-structured manner. Markov chain Monte Carlo (MCMC) methods are demonstrated which can be used to sample from the posterior distribution over trees containing variable numbers of hidden units.

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Text

Williams. "A MCMC Approach to Hierarchical Mixture Modelling." Neural Information Processing Systems, 1999.

Markdown

[Williams. "A MCMC Approach to Hierarchical Mixture Modelling." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/williams1999neurips-mcmc/)

BibTeX

@inproceedings{williams1999neurips-mcmc,
  title     = {{A MCMC Approach to Hierarchical Mixture Modelling}},
  author    = {Williams, Christopher K. I.},
  booktitle = {Neural Information Processing Systems},
  year      = {1999},
  pages     = {680-686},
  url       = {https://mlanthology.org/neurips/1999/williams1999neurips-mcmc/}
}