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Computational Methods for Mixture of Dirichlet Process Models
1998This chapter lays out the basic computational strategies for models based on mixtures of Dirichlet processes. I describe the basic algorithm and give advice on how to improve this algorithm through a collapse of the state space of the Markov chain and through blocking of variates for generation.
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Marginal Likelihood and Bayes Factors for Dirichlet Process Mixture Models
Journal of the American Statistical Association, 2003Siddhartha Chib
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Axially Symmetric Data Clustering Through Dirichlet Process Mixture Models of Watson Distributions
IEEE Transactions on Neural Networks and Learning Systems, 2019Wentao Fan, Nizar Bouguila, Jixiang Du
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On Selecting a Prior for the Precision Parameter of Dirichlet Process Mixture Models
Journal of Statistical Planning and Inference, 2009Robert M Dorazio
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Nonparametric Bayesian Lifetime Data Analysis Using Dirichlet Process Lognormal Mixture Model
Naval Research Logistics, 2013Tao Yuan
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IEEE Transactions on Neural Networks and Learning Systems, 2019
Yi-Zhe Song, W Kleijn, Zhanyu Ma
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Yi-Zhe Song, W Kleijn, Zhanyu Ma
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Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model
IEEE Transactions on Industrial Informatics, 2018Lyudmila Mihaylova, Zhenglin Li
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Selecting the Precision Parameter Prior in Dirichlet Process Mixture Models
Journal of Statistical Planning and Inference, 2012Trevor J. Sweeting
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Unsupervised Tracking With the Doubly Stochastic Dirichlet Process Mixture Model
IEEE Transactions on Intelligent Transportation Systems, 2016Edmund Y. Lam, Nelson H C Yung
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