The large-sample properties of likelihood-based statistical inference under mixture models have received much attention from statisticians. Although the consistency of the nonparametric MLE is regarded as a standard conclusion, many researchers ignore the precise conditions required on the mixture model. An incorrect claim of consistency can lead to false conclusions even if the mixture model u...

We present a construction and basic properties of a class of continuous distributions of an arbitrary form defined on a compact (bounded) set by concatenating in a continuous manner three probability density functions with bounded support using a modified mixture technique. These three distributions may represent growth, stability and decline stages of a physical or mental phenomenon.

The performance of interval estimates in a uniform-beta mixture model is evaluated using three computational strategies. Such a model has found use when modeling a distribution of P -values from multiple testing applications. The number of P -values and the closeness of a parameter to the boundary of its space both play a role in the precision of parameter estimates as does the “nearness” of th...

We present a new approach to learning probabilistic models for high dimensional data. This approach divides the data dimensions into low dimensional subspaces, and learns a separate mixture model for each subspace. The models combine in a principled manner to form a flexible modular network that produces a total density estimate. We derive and demonstrate an iterative learning algorithm that us...

In forensic speaker recognition, the strength of evidence is estimated using the likelihood ratio, which is the relative probability of observing the evidence, given the hypothesis that the suspect is the source of the questioned recording and the hypothesis that anyone else in a relevant potential population is its source. In order to calculate the likelihood ratio we use two approaches; one, ...

The Dirichlet process is a prior used in nonparametric Bayesian models of data, particularly in Dirichlet process mixture models (also known as infinite mixture models). It is a distribution over distributions, i.e. each draw from a Dirichlet process is itself a distribution. It is called a Dirichlet process because it has Dirichlet distributed finite dimensional marginal distributions, just as...

A system is described that provides robust and real-time focus-of-attention for tracking and segmentation of multicoloured objects. Gaussian mixture models were used to estimate the probability densities of object foreground and scene background colours. Tracking was performed by tting dynamic bounding boxes to image regions of maximum probability. Two scenarios are presented: (1) real-time fac...

This work presents a set of three simple and explicit equations as a function of temperature, pressure, and mass fraction for calculation of the entropy of the ammonia-water mixture in saturated and super heated conditions. They are intended for use in the optimization and second law efficiency of absorption processes. The equations are constructed by the least square method for curve fitting u...