Finite mixture models are statistical models which appear in many problems in statistics and machine learning. In such models it is assumed that data are drawn from random probability measures, called mixture components, which are themselves drawn from a probability measure P over probability measures. When estimating mixture models, it is common to make assumptions on the mixture components, s...

Background: In 2011 the Progress in International Reading Literacy Study (PIRLS) and the Trends in International Mathematics and Science Study (TIMSS) were conducted at fourth grade in a number of participating countries with a shared representative sample. In this article we investigate whether there are multidimensional proficiency patterns across the competency domains or not. Methods: In or...

We use colour mixture models for real-time colour-based object localisation, tracking and segmentation in dynamic scenes. Within such a framework, we address the issues of model order selection, modelling scene background and model adaptation in time. Experimental results are given to demonstrate our approach in diierent scale and lighting conditions.

We compare four different approaches towards modeling frame-level errors in GSM channels. One of these, the Markov-based Trace Analysis model (MTA), was developed for the purpose of modeling a GSM channel. The next two, -th-order Markov models and hidden Markov models (HMMs) have been widely used to model loss in wired networks. All three of these have difficulty modeling empirical GSM framelev...

Recently we have proposed Gaussian mixtures as a local statistical model to synthesize artificial textures. We describe the statistical dependence of pixels of a movable window by multivariate Gaussian mixture of product components. The mixture components correspond to different variants of image patches as they appear in the window. In this sense they can be used to identify different segments...

For simplicity we will focus on a simple Gaussian Mixture Model. Consider a mixture of k spherical gaussians in R which is the following generative process. Let w ∈ ∆([k]) denote a distribution and let μ1, . . . , μk ∈ R be the mean vectors. Each point xi is generated by first choosing a component hi ∼ w and then xi ∼ N (μhi , I). We are given n samples x1, . . . , xn drawn according to this pr...

We study a two-component semiparametric mixture model where one component distribution belongs to a parametric class, while the other is symmetric but otherwise arbitrary. This semiparametric model has wide applications in many areas such as large-scale simultaneous testing/multiple testing, sequential clustering, and robust modeling. We develop a class of estimators that are surprisingly simpl...

This paper presents the results of Persian handwritten word recognition based on Mixture of Experts technique. In the basic form of ME the problem space is automatically divided into several subspaces for the experts, and the outputs of experts are combined by a gating network. In our proposed model, we used Mixture of Experts Multi Layered Perceptrons with Momentum term, in the classification ...

The Irish college admissions system involves prospective students listing up to ten courses in order of preference on their application. Places in third level educational institutions are subsequently offered to the applicants on the basis of both their preferences and their final second level examination results. The college applications system is a large area of public debate in Ireland. Detr...

A Bayesian-based methodology is presented which automatically penalizes overcomplex models being fitted to unknown data. We show that, with a Gaussian mixture model, the approach is able to select an “optimal” number of components in the model and so partition data sets. The performance of the Bayesian method is compared to other methods of optimal model selection and found to give good results...