Mixture transition distribution (MTD) time series models build high-order dependence through a weighted combination of first-order densities for each one specified number lags. We present framework to construct stationary MTD that extend beyond linear, Gaussian dynamics. study conditions strict stationarity which allow different constructions with either continuous or discrete families the give...

The proposed project is an implementation of speaker recognition systems, both identification and verification. The systems are built using Gaussian Mixture Models, as proposed in several papers from Douglas A. Reynolds. The use of Fractional Covariance Matrix is studied as an possible increase for the traditional recognition systems. keywords: speaker recognition; Gaussian Mixture Models; like...

This paper describes a flexible non-Gaussian statistical method used to model polarimetric synthetic aperture radar (POLSAR) data. We outline the theoretical basis of the well-know product model as described by the class of Scale Mixture models and discuss their appropriateness for modelling radar data. The statistical distributions of several Scale mixture models are then described, including ...

Mixture models, especially mixtures of Gaussian, have been widely used due to their great flexibility and power. Non-Gaussian clusters can be approximated by several Gaussian components, however, it can not always acquire appropriate results. By cancelling the nonnegative constraint to mixture coefficients and introducing a new concept of “negative components”, we extend the traditional mixture...

This study discusses some variants of Ordered WeightedAveraging (OWA) operators and related information aggregation methods. Indetail, we define the Extended Ordered Weighted Sum (EOWS) operator and theExtended Ordered Weighted Averaging (EOWA) operator, which are applied inscientometrics evaluation where the preference is over finitely manyrepresentative works. As...

The objective of this tutorial is to introduce basic concepts of a Hidden Markov Model (HMM) as a fusion of more simple models such as a Markov chain and a Gaussian mixture model. The tutorial is intended for the practicing engineer, biologist, linguist or programmer who would like to learn more about the above mentioned fascinating mathematical models and include them into one’s repertoire. Th...

This paper proposes the use of Gaussian Mixture Models to estimate conditional probability density functions in an environmental risk mapping context. A conditional Gaussian Mixture Model has been compared to the geostatistical method of Sequential Gaussian Simulations and shows good performances in reconstructing local PDF. The data sets used for this comparison are parts of the digital elevat...

We have previously proposed a cross-validation (CV) based Gaussian mixture optimization method that efficiently optimizes the model structure based on CV likelihood. In this study, we propose aggregated cross-validation (AgCV) that introduces a bagging-like approach in the CV framework to reinforce the model selection ability. While a single model is used in CV to evaluate a held-out subset, Ag...