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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)...
On a Rapid Simulation of the Dirichlet Process
Dirichlet process Gamma process L´ evy measure Stick-breaking representation
2011/7/19
We describe a simple and efficient procedure for approximating the L\'evy measure of a $\text{Gamma}(\alpha,1)$ random variable.
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 ...
Mean field inference for the Dirichlet process mixture model
Bayesian nonparametrics approximation methods variational inference density estimation
2009/9/16
We present a systematic study of several recently proposed methods of mean field inference for the Dirichlet process mixture (DPM) model. These methods provide approximations to the posterior distribu...
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...
Distribution of a random functional of a Ferguson-Dirichlet process over the unit sphere
Kuo multivariate c-characteristic function Ferguson-Dirichlet process
2009/3/20
Jiang, Dickey, and Kuo [12] gave the multivariate c-characteristic function and showed that it has properties similar to those of the multivariate Fourier transformation. We first give the multivariat...
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...