Dirichlet process mixture - Open Access .click

SIAM Journal on Scientific and Statistical Computing, 1986
The computation of Bayes estimators based on mixtures of Dirichlet processes is treated. These estimators may be written as ratios of two multidimensional integrals, each of which may be decomposed into a weighted average of products of one-dimensional integrals.
Lynn Kuo
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Dependent Mixtures of Dirichlet Processes

Computational Statistics and Data Analysis, 2011
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Stephen G. Walker +2 more
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A Dirichlet Process Mixture of Generalized Dirichlet Distributions for Proportional Data Modeling

IEEE Transactions on Neural Networks, 2010
In this paper, we propose a clustering algorithm based on both Dirichlet processes and generalized Dirichlet distribution which has been shown to be very flexible for proportional data modeling. Our approach can be viewed as an extension of the finite generalized Dirichlet mixture model to the infinite case.
Nizar Bouguila
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Robust Dirichlet Process mixtures

2011 Seventh International Conference on Natural Computation, 2011
Non-parametric Dirichlet Process mixture (DPM) approaches for density estimation and clustering allow for automatic model selection. In this paper, we aim to develop robust DPM algorithm for clustering datasets with scatter objects, or outliers. In the developed mean-field variational inference algorithms, the auxiliary posterior distributions are ...
Jianyong Sun, Jonathan M. Garibaldi
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dirichlet process
dirichlet process mixture model
clustering

mixture model
machine learning
medicine

probabilities
mathematical statistics