2010
NeurIPS
NeurIPS 2010
Shadow Dirichlet for Restricted Probability Modeling
Abstract
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.
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Trend Setter
- Density Estimation
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Keyword Pioneer
- dirichlet distribution
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Hot Topic Early Bird
- probabilistic modeling
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Representation Learning
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Bayesian & Probabilistic > Variational Inference
Machine Learning > Learning Types > Parameter Estimation
Machine Learning > Core Methods > Density Estimation