background: study in design to incorporate accelerated proliferation correction factors into linearquadratic and multiple-component models. materials and methods: accelerated proliferation rate correction factor has been incorporated into the linearquadratic and the multiple component models by applying accelerated exponential cell growth to explain the tumor cell kinetics and estimates proper ...

This paper proposes a general class of regression models for continuous proportions when the data contain zeros or ones. The proposed class of models assumes that the response variable has a mixed continuous-discrete distribution with probability mass at zero or one. The beta distribution is used to describe the continuous component of the model, since its density has a wide range of different ...

Contents 1 Introduction 2 The R program 3 Phenology, an introduction 4 Geo-statistical modelling & analysis of phenological phases 5 Multivariate tools to visualise spatio-temporal patterns in phenology 6 Gaussian Mixture Models of geographical phenomena 7 Analysis of space-time correlations within geographical studies

High angular resolution diffusion imaging is an increasingly important image modality. The nature of the diffusion data makes mixtures of probability distributions particularly suitable for modeling its signals. In this paper, we introduce Bayesian finite mixture models for studying the diffusion field. We apply a spatially variant mixture model to study prior distributions on the model paramet...

Mixture models constitute a popular type of probabilistic neural networks which model the density of a dataset using a convex combination of statistical distributions, with the Gaussian distribution being the one most commonly used. In this work we propose a new probability density function, called the Π-sigmoid, from its ability to form the shape of the letter “Π” by appropriately combining tw...

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

The purpose of this paper is twofold. Firstly, to assess the merit of estimating probability density functions rather than level or classification estimations on a oneday-ahead forecasting task of the EUR/USD time series. This is implemented using a Gaussian mixture model neural network, benchmarking the results against standard forecasting models, namely a naïve model, a moving average converg...

Hidden Markov model speech recognition systems typically use Gaussian mixture models to estimate the distributions of decorrelated acoustic feature vectors that correspond to individual subword units. By contrast, hybrid connectionist-HMM systems use discriminatively-trained neural networks to estimate the probability distribution among subword units given the acoustic observations. In this wor...

Hidden Markov model speech recognition systems typically use Gaussian mixture models to estimate the distributions of decorrelated acoustic feature vectors that correspond to individual subword units. By contrast, hybrid connectionist-HMM systems use discriminatively-trained neural networks to estimate the probability distribution among subword units given the acoustic observations. In this wor...

In task switching, increasing the response-cue interval has been shown to reduce the switch cost. This has been attributed to a time-based decay process influencing the activation of memory representations of tasks (task-sets). Recently, an alternative account based on interference rather than decay has been successfully applied to this data (Horoufchin et al., 2011a). In this account, variatio...