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Nonparametric Baysian Biclustering with a Double Mixture Model
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NPBB is a Nonparameteric Bayesian Biclustering Model, inference
is performed by collapsed Gibbs sampling. We support Gaussian
and Bernoulli random variables for the matrix elements.

The best way to get started is by running the examples under demos/


The Bernoulli case is the same as discussed in
Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T. & Ueda, N. (2006),
Learning systems of concepts with an infinite relational model,
Proceedings of the 21st National Conference on Artificial Intelligence.

The Gaussian case is discussed in:
Edward Meeds and Sam Roweis,
Nonparametric Bayesian Biclustering,
UTML-TR-2007-001, Technical Report, University of Toronto, 2007.

Coypright and Authorship

The code in NPBBGibbs is released under the GNU license, some of the
code in tools is developed by a third-party.

Patrick Pletscher
Machine Learning Group
ETH Zurich
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