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...

This paper presents a new probability table memory compression method based on mixture models and its application to N-tuple recognizers and N-gram character language models. Joint probability tables are decomposed into lower dimensional probability components and their mixtures. The maximum likelihood parameters of the mixture models are trained by the Expectation Maximization (EM) algorithm a...

We present a method for condensing the information in multiple alignments of proteins into a mixture of Dirichlet densities over amino acid distributions. Dirichlet mixture densities are designed to be combined with observed amino acid frequencies to form estimates of expected amino acid probabilities at each position in a proole, hidden Markov model, or other statistical model. These estimates...

A markovian model of the error probability density for decision feedback equalizer is proposed and its application to the error propagation probability computation is derived. The model is a generalization of the Lütkemeyer and Noll model proposed in [1]. It is obtained by the analysis of the gaussian mixture distribution of the errors which follows a Markov Process. The analysis of this proces...

PURPOSE This work describes a spatially variant mixture model constrained by a Markov random field to model high angular resolution diffusion imaging (HARDI) data. Mixture models suit HARDI well because the attenuation by diffusion is inherently a mixture. The goal is to create a general model that can be used in different applications. This study focuses on image denoising and segmentation (pr...

We present an unsupervised classification algorithm based on an ICA mixture model. A mixture model is a model in which the observed data can be categorized into several mutually exclusive data classes. In an ICA mixture model, it is assumed that the data in each class are generated by a linear mixture of independent sources. The algorithm finds the independent sources and the mixing matrix for ...

Abstract. Recently stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. Also image segmentation means to divide one picture into different types of classes or regions, for example a picture of geometric shapes has some classes with different colors such as ’circle’, ’rectangle’, ’triangle’ and so ...

An optimal Bayesian classifier using mixture distribution class models with joint learning of loss and prior probability functions is proposed for automatic land cover classification. The probability distribution for each land cover class is more realistically modeled as a population of Gaussian mixture densities. A novel two-stage learning algorithm is proposed to learn the Gaussian mixture mo...

This paper presents a new spectral modeling and conversion method for voice conversion. In contrast to the conventional Gaussian mixture model (GMM) based methods, we use restricted Boltzmann machines (RBMs) as probability density models to model the joint distributions of source and target spectral features. The Gaussian distribution in each mixture of GMM is replaced by an RBM, which can bett...