Finite mixture models can be used in estimating complex, unknown probability distributions and also in clustering data. The parameters of the models form a complex representation and are not suitable for interpretation purposes as such. In this paper, we present a methodology to describe the finite mixture of multivariate Bernoulli distributions with a compact and understandable description. Fi...

Probability density function estimation from limited data sets is a classical problem in pattern recognition. In this paper we propose a reformulation of the well-known nonparametric Parzen method as a parametrically regularized Gaussian Mixture Model, from which we can easily estimate density contour level. As an application illustration to the proposed contour level estimator, we also address...

Stable isotopes are a powerful tool for ecologists, often used to assess contributions of different sources to a mixture (e.g. prey to a consumer). Mixing models use stable isotope data to estimate the contribution of sources to a mixture. Uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. We developed a Bayesian-mixing model that ...

Detectability of individual animals is highly variable and nearly always < 1; imperfect detection must be accounted for to reliably estimate population sizes and trends. Hierarchical models can simultaneously estimate abundance and effective detection probability, but there are several different mechanisms that cause variation in detectability. Neglecting temporary emigration can lead to biased...

Image segmentation plays a major role in quantitative image analysis and computer aided detection (CAD) and diagnosis (CADx) for clinical applications. Conventional segmentation assigns a single label to each voxel, neglecting the partial volume (PV) effect. This work presents an EM (Expectation Maximization) framework for segmentation of tissue mixture in each voxel. Image data and tissue mixt...

In this paper we consider a Bayesian analysis of contingency tables allowing for the possibility that cells may have probability zero. In this sense we depart from standard log-linear modeling that implicitly assumes a positivity constraint. Our approach leads us to consider mixture models for contingency tables, where the components of the mixture, which we call model-instances, have distinct ...

In order to achieve the maximum flexibility in adaptation to ever changing customer’s expectations in customer relationship management, appropriate measures of customer behavior should be continually monitored. To this end, control charts adjusted for buyer’s/visitor’s prior intention to repurchase or visit again are suitable means taking into account the heterogeneity across customers. In the ...