<p>In this thesis, we develop some Bayesian mixture density estimation for univariate and multivariate data. We start proposing a repulsive process favoring mixture components further apart. While conducting inferences on the cluster-specific parameters, current frequentist and Bayesian methods often encounter problems when clusters are placed too close together to be scientifically meaningful. Current Bayesian practice generates component-specific parameters independently from a common prior, which tends to favor similar components and often leads to substantial probability assigned to redundant components that are not needed to fit the data. As an alternative, we propose to generate components from a repulsive process, which leads to fe...
A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algor...
This thesis develops models and associated Bayesian inference methods for flexible univariate and mu...
Abstract This thesis develops models and associated Bayesian inference methods for flexible univaria...
<p>Although Bayesian density estimation using discrete mixtures has good performance in modest dimen...
Latent variable models are used extensively in unsupervised learning within the Bayesian paradigm, t...
<p>Clustering methods are designed to separate heterogeneous data into groups of similar objects suc...
Mixture models are widely used in many application areas, with finite mixtures of Gaussian distribut...
Mixture models are a standard tool in statistical analysis, widely used for density modeling and mod...
AbstractOur first focus is prediction of a categorical response variable using features that lie on ...
<p>Mixture modeling of continuous data is an extremely effective and popular method for density esti...
In a society which produces and consumes an ever increasing amount of information, methods which can...
A method for implicit variable selection in mixture-of-experts frameworks is proposed. We introduce...
Abstract. Bayesian approaches to density estimation and clustering using mixture distributions allow...
Multiple scale distributions are multivariate distributions that exhibit a variety of shapes not nec...
We develop a general class of Bayesian repulsive Gaussian mixture models that encourage well-separat...
A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algor...
This thesis develops models and associated Bayesian inference methods for flexible univariate and mu...
Abstract This thesis develops models and associated Bayesian inference methods for flexible univaria...
<p>Although Bayesian density estimation using discrete mixtures has good performance in modest dimen...
Latent variable models are used extensively in unsupervised learning within the Bayesian paradigm, t...
<p>Clustering methods are designed to separate heterogeneous data into groups of similar objects suc...
Mixture models are widely used in many application areas, with finite mixtures of Gaussian distribut...
Mixture models are a standard tool in statistical analysis, widely used for density modeling and mod...
AbstractOur first focus is prediction of a categorical response variable using features that lie on ...
<p>Mixture modeling of continuous data is an extremely effective and popular method for density esti...
In a society which produces and consumes an ever increasing amount of information, methods which can...
A method for implicit variable selection in mixture-of-experts frameworks is proposed. We introduce...
Abstract. Bayesian approaches to density estimation and clustering using mixture distributions allow...
Multiple scale distributions are multivariate distributions that exhibit a variety of shapes not nec...
We develop a general class of Bayesian repulsive Gaussian mixture models that encourage well-separat...
A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algor...
This thesis develops models and associated Bayesian inference methods for flexible univariate and mu...
Abstract This thesis develops models and associated Bayesian inference methods for flexible univaria...