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Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
Dynamic Clustering Asymptotics Dependent Dirichlet Process Mixture
2013/6/17
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...
Quantum Annealing for Dirichlet Process Mixture Models with Applications to Network Clustering
Quantum annealing Dirichlet process Stochastic optimization Maximum a posteriori estimation Bayesian nonparametrics
2013/6/17
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)...
Graphically dependent and spatially varying Dirichlet process mixtures
local clustering global clustering mixture models nonparametric Bayes Dirichletprocess Gaussian process graphical model spatial dependence
2010/3/9
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...
Hidden Markov Dirichlet Process: Modeling Genetic Inference in Open Ancestral Space
Dirichlet Process Hierarchical DP hidden Markov model MCMC statistical genetics recombination population structure SNP
2009/9/22
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...
Splitting and Merging Components of a Nonconjugate Dirichlet Process Mixture Model
Bayesian model Markov chain Monte Carlo split-merge moves nonconjugate prior
2009/9/22
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...
Variational inference for Dirichlet process mixtures
Dirichlet processes hierarchical models variational inference image processing Bayesian computation
2009/9/21
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 ...
Some Diffusion Processes Associated With Two Parameter Poisson-Dirichlet Distribution and Dirichlet Process
Some Diffusion Processes Two Parameter Poisson-Dirichlet Distribution Dirichlet Process
2010/3/19
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...
Nonlinear Models Using Dirichlet Process Mixtures
Nonlinear Models Dirichlet Process Mixtures
2010/4/27
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...
Bayesian Inference for Linear Dynamic Models with Dirichlet Process Mixtures
Bayesian nonparametrics Dirichlet Process Mixture Markov Chain Monte Carlo Rao-Blackwellization Particle filter
2010/4/26
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...