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Small Variance Asymptotics for Dirichlet Process Mixtures of SVMs
Small Variance Asymptotics Dirichlet Process Mixtures SVM
2016/1/22
Infinite SVM (iSVM) is a Dirichlet process (DP) mix-ture of large-margin classifiers. Though flexible in learning nonlinear classifiers and discovering latent clustering structures, iSVM has a difficu...
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...
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 ...
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...