Bayesian estimation of the parameters in beta mixture models (BMM) is analytically
intractable. The numerical solutions to simulate the posterior distribution are available, but …

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two
fundamental invertible transformations, namely, serial nonlinear transformation and parallel …

A novel Bayesian matrix factorization method for bounded support data is presented. Each
entry in the observation matrix is assumed to be beta distributed. As the beta distribution has …

A new regression model for proportions is presented by considering the Beta rectangular
distribution proposed by Hahn (2008). This new model includes the Beta regression model …

This paper presents an unsupervised approach for feature selection and extraction in
mixtures of generalized Dirichlet (GD) distributions. Our method defines a new mixture …

In this paper, we consider the problem of constructing accurate and flexible statistical
representations for count data, which we often confront in many areas such as data mining …

The work proposed in this paper is motivated by the need to develop powerful models and
approaches to classify and learn proportional data. Indeed, an abundance of interesting …

We introduce a Bayesian approach to predictive density calibration and combination that
accounts for parameter uncertainty and model set incompleteness through the use of …

Several approaches have been proposed for the problem of identifying authoritative actors
in online communities. However, the majority of existing methods suffer from one or more of …

We consider Service-Oriented Computing (SOC) environments. Such environments are
populated with services that stand proxy for a variety of information resources. A …