This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian
framework for modeling axial data (ie, observations are axes of direction) that can be …

In this paper, we develop a novel variational Bayesian learning method for the Dirichlet
process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very …

We present a truncation-free online variational inference algorithm for Bayesian
nonparametric models. Unlike traditional (online) variational inference algorithms that …

This paper addresses the Bayesian estimation of the von-Mises Fisher (vMF) mixture model
with variational inference (VI). The learning task in VI consists of optimization of the …

The Dirichlet process prior allows flexible nonparametric mixture modeling. The number of
mixture components is not specified in advance and can grow as new data arrive. However …

The classical mixture of Gaussians model is related to K-means via small-variance
asymptotics: as the covariances of the Gaussians tend to zero, the negative log-likelihood of …

In recent years the Dirichlet process prior has experienced a great success in the context of
Bayesian mixture modeling. The idea of overcoming discreteness of its realizations by …

Empirical models that relate multiple quality features to a set of design variables play a vital
role in many industrial process optimization methods. Many of the current modeling methods …

In many practical situations, a parametric model cannot be expected to describe in an
appropriate manner the chance mechanism generating an observed dataset, and unrealistic …

Current methods for learning graphical models with latent variables and a fixed structure
estimate optimal values for the model parameters. Whereas this approach usually produces …