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Shadow Dirichlet for Restricted Probability Modeling
Bela A Frigyik · Maya R Gupta · Yihua Chen

Wed Dec 08 05:15 PM -- 05:20 PM (PST) @ Regency Ballroom

Although the Dirichlet distribution is widely used, the independence structure of its components
limits its accuracy as a model. The proposed shadow Dirichlet distribution manipulates
the support in order to model probability mass functions (pmfs) with dependencies or constraints that
often arise in real world problems, such as regularized pmfs, monotonic pmfs, and pmfs with bounded
variation. We describe some properties of this new class of
distributions, provide maximum entropy constructions,
give an expectation-maximization method for estimating the mean parameter, and illustrate with real data.

Author Information

Bela A Frigyik (University of Washington)
Maya R Gupta (University of Washington)
Yihua Chen (University of Washington)

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