Modeling of a large joint probability table is problematic when its variables have a large number of categories. In such a case, a mixture of simpler probability tables could be a good model. And the estimation of such a large probability table frequently has another problem of data sparseness. When constructing mixture models with sparse data, EM estimators based on the β-likelihood are expect...

A semi-continuous segmental probability model, which can be considered as a special form of continuous mixture segmental probability model with continuous output probability density functions sharing in a mixture Gaussian density codebook, is proposed in this paper. The amount of training data required, as well as the computational complexity of the semi-continuous segmental probability model(S...

The fMRI visual field map (VFM) is obtained by using a rotating-expanding visual target to identify the area of the retina that corresponds to activated visual cortex. Since fMRI data is (1) relative rather than absolute and (2) has a degree of noise that may mask the activation, identifying differences in VFMs requires a model that will differentiate changes in the underlying structure from di...

Model based cluster analysis is often carried out by estimation of the parameters of a normal mixture. But mixture components do not necessarily reeect the idea of a \cluster". I discuss how to formalize the concept of \clusters" w.r.t. probability distributions on the real line by means of xed point clusters, i.e., sets that do not contain any outlier and with respect to which the rest of the ...

A class of probability models for inference about alterations in gene expression is reviewed. The class entails discrete mixing over patterns of equivalent and differential expression among different mRNA populations, continuous mixing over latent mean expression values conditional on each pattern, and variation of data conditional on latent means. An R package EBarrays implements inference cal...

The recent spate of theoretical models of behaviour under ambiguity can be partitioned into two sets: those involving multiple priors (in which the probabilities of the various events are not known but probabilities can be attached to the various possible values for the probabilities) and those not involving multiple priors. This paper concentrates on the first set and provides an experimental ...

This work investigates a combined stochastic and deterministic optimization approach for multivariate mixture density estimation. Mixture probability density models are selected and optimized by combining the optimization characteristics of a multiagent stochastic optimization algorithm based on evolutionary programming and the expectation-maximization algorithm. Unlike the traditional finite m...

The cumulative leukemia-free survival rate of refractory anemia with excess of blasts was fitted to three parametric failure time models, i.e., the usual exponential and Weibull, and exponential mixture models. Among the three, the best fit model was the exponential mixture model, which was 119 times more likely to assume that samples came from the exponential mixture distribution than to assum...

Mixture probability density functions had recently been proposed to describe some fertility patterns characterized by a bi-modal shape. These mixture probability density functions appear to be adequate when the fertility pattern is actually bi-modal but less useful when the shape of age-specific fertility rates is unimodal. A further model is proposed based on skew-symmetric probability density...