In this paper, we introduce the R package gendist that computes the probability density function, the cumulative distribution function, the quantile function and generates random values for several generated probability distribution models including the mixture model, the composite model, the folded model, the skewed symmetric model and the arc tan model. These models are extensively used in th...

We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appea...

In this paper we present a strategy for producing low-dimensional projections that maximally separate the classes in Gaussian Mixture Model classification. The most revealing subspaces are those along which the classes are maximally separable. Here we consider a particular probability product kernel as a measure of similarity or affinity between the class conditional distributions. It takes an ...

MOTIVATION Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particular, model-based clustering assumes that the data is generated by a finite mixture of underlying pro...

Robust single-user detection is employed in a DS/CDMA system in which the noise proc(ess cent ains impulsive components. The breakdown point is computed for a mixture noise model. Noise rather than interference is shown to be the primary obstacle in achieving good performance when the signal power is low. The bit error probability expressions are also derived under a Gaussian mixture, DS/CDMA e...

Underwater multi-targets tracking has always been a difficult problem in active sonar tracking systems. In order to estimate the parameters of time-varying multi-targets moving in underwater environments, based on the Bayesian filtering framework, the Random Finite Set (RFS) is introduced to multi-targets tracking, which not only avoids the problem of data association in multi-targets tracking,...

A finite mixture model is used to describe the diameter distributions of mixed-species forest stands. A three-parameter Weibull function is assumed as the component probability density function in the finite mixture model. Four example plots, each with two species, are selected to demonstrate model fitting and comparison. It appears that the finite mixture model is flexible enough to fit irregu...

Statistical methods have certain advantages which advocate their use in pattern recognition. One central problem in statistical methods is estimation of class conditional probability density functions based on examples in a training set. In this study maximum likelihood estimation methods for Gaussian mixture models are reviewed and discussed from a practical point of view. In addition, good pr...

The primary goal of multivariate statistical process performance monitoring is to identify deviations from normal operation within a manufacturing process. The basis of the monitoring schemes is historical data that has been collected when the process is running under normal operating conditions. This data is then used to establish confidence bounds to detect the onset of process deviations. In...

Function approximation in online, incremental, reinforcement learning needs to deal with two fundamental problems: biased sampling and nonstationarity. In this kind of task, biased sampling occurs because samples are obtained from specific trajectories dictated by the dynamics of the environment and are usually concentrated in particular convergence regions, which in the long term tend to domin...