Foundations of Nonparametric Bayesian Methods (Part III)
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If you have a question about this talk, please contact Peter Orbanz.
This 3part tutorial will address a machine learning audience, not
assumed to be familiar with measure theory or the theory stochastic
processes. The course is intended to provide (1) an overview of what
nonparametric Bayesian models exist beyond those already used in
machine learning, and (2) a basic understanding of the mathematical
construction of ’’process’’ models, both existing ones and new models
on a variety of possible domains.
Part III : Construction of new models
Recent works in machine learning consider the construction of models
on other domains than the simplex, that is, models which
nonparametrically generate objects other than probability
distributions (such as the binary matrices generated by the Indian
Buffet Process). We discuss how nonparametric Bayesian models can be
constructed on arbitrary domains, and what limitations we will have to
expect for such constructions.
Webpage:
http://mlg.eng.cam.ac.uk/porbanz/npbtutorial.html
This talk is part of the Machine Learning @ CUED series.
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