Abstract Gaussian Process Latent Variable Model (GPLVM), as a flexible bayesian non-
parametric modeling method, has been extensively studied and applied in many learning …

The mixture of Gaussian processes (MGP) is an important probabilistic model which is often
applied to the regression and classification of temporal data. But the existing EM algorithms …

Dirichlet process (DP) mixture models provide a flexible Bayesian framework for density
estimation. Unfortunately, their flexibility comes at a cost: inference in DP mixture models is …

The Dirichlet distribution is a multivariate generalization of the Beta distribution. It is an
important multivariate continuous distribution in probability and statistics. In this report, we …

Parameter estimation of probability density functions is one of the major steps in the area of
statistical image and signal processing. In this paper we explore several properties and …

A dependent hierarchical beta process (dHBP) is developed as a prior for data that may be
represented in terms of a sparse set of latent features, with covariate-dependent feature …

This paper studies the problem of deriving fast and accurate classification algorithms with
uncertainty quantification. Gaussian process classification provides a principled approach …

Dirichlet process mixture of Gaussians (DPMG) has been used in the literature for clustering
and density estimation problems. However, many real-world data exhibit cluster distributions …

Data driven soft sensors have found widespread applications in chemical processes for
predicting those important yet difficult-to-measure quality variables. In the vast majority of …

The probabilistic radial basis function (PRBF) net-work constitutes a probabilistic version of
the RBF network for classification that extends the typical mixture model approach to …