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This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters. The alg...
We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM) models based on the Chinese restaurant process (CRP). QA is a parallelized extension of simulated annealing (SA)...
We consider the problem of clustering grouped and functional data, which are indexed by a covariate, and assessing the dependency of the clustered groups on the covariate. We assume that each observ...
We present a new statis- tical framework called hidden Markov Dirichlet process (HMDP) to jointly model the genetic recombinations among a possibly innite number of founders and the coalescence-wit...
The inferential problem of associating data to mixture components is dif- ficult when components are nearby or overlapping. We introduce a new split-merge Markov chain Monte Carlo technique that eff...
Dirichlet process (DP) mixture models are the cornerstone of non- parametric Bayesian statistics, and the development of Monte-Carlo Markov chain (MCMC) sampling methods for DP mixtures has enabled ...
The two parameter Poisson-Dirichlet distribution PD( , ) is the distribution of an infinite dimensional random discrete probability. It is a generalization of Kingman’s Poisson-Dirichlet distributi...
We introduce a new nonlinear model for classification, in which we model the joint distribution of response variable, y, and covariates, x, non-parametrically using Dirichlet process mixtures. We kee...
Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian statespace models. We address here the case where the noise probability density functions are of unknown functi...

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