Mixture models are one of the most widely used statistical tools when dealing with data from heterogeneous populations. Following a Bayesian nonparametric perspective, we introduce new class priors: Normalized Independent Point Process. We investigate probabilistic properties this and present many special cases. In particular, provide an explicit formula for distribution implied partition, as w...

We propose a novel estimator for the number of mixture components (denoted by M) in nonparametric finite model. The setting that we consider is one where analyst has repeated observations K?2 variables are conditionally independent given finitely supported latent variable with M support points. Under mild assumption on joint distribution observed and variables, show an integral operator T ident...

During the past decade, finite mixture modeling has become a well-established technique in data analysis and clustering. This paper focus on developing a variational inference framework to learn finite Beta-Liouville mixture models that have been proposed recently as an efficient way for proportional data clustering. In contrast to the conventional expectation maximization (EM) algorithm, commo...

Spatial Finite Non-Gaussian Mixtures for Color Image Segmentation Ali Sefidpour Finite mixture models are one of the most widely and commonly used probabilistic techniques for image segmentation. Although the most well known and commonly used distribution when considering mixture models is the Gaussian, it is certainly not the best approximation for image segmentation and other related image pr...

We developed a new semi-supervised EM-like algorithm that is given the set of objects present in eachtraining image, but does not know which regions correspond to which objects. We have tested thealgorithm on a dataset of 860 hand-labeled color images using only color and texture features, and theresults show that our EM variant is able to break the symmetry in the initial solution. We compared...

We propose an efficiently structured nonlinear finitememory filter for denoising (filtering) a Gaussian signal contaminated by additive impulsive colored noise. The noise is modeled as a zero-mean Gaussian mixture (ZMGM) process. We first derive the optimal estimator for the static case, in which a Gaussian random variable (RV) is contaminated by an impulsive ZMGM RV. We provide an analytical d...