We present the bump mixture model, a statistical model for analog data where the probabilistic semantics, inference, and learning rules derive from low-level transistor behavior. The bump mixture model relies on translinear circuits to perform probabilistic inference, and floating-gate devices to perform adaptation. This system is low power, asynchronous, and fully parallel, and supports variou...

We have previously proposed a trajectory model which is based on a mixture density network (MDN) trained with target variables augmented with dynamic features together with an algorithm for estimating maximum likelihood trajectories which respects the constraints between those features. In this paper, we have extended that model to allow diagonal covariance matrices and multiple mixture compone...

A mixture model is a weighted combination of probability distributions. We consider the problem of identifying the component distributions of a mixture model by examining random samples from the mixture. Our main result is that a simple spectral algorithm for learning a mixture of k spherical Gaussians in n-dimensions works remarkably well — it succeeds in identifying the Gaussians assuming ess...

This paper is concerned with estimating a probability density function of human skin color using a nite Gaussian mixture model whose parameters are estimated through the EM algorithm Hawkins statistical test on the normality and homoscedasticity common covariance matrix of the estimated Gaussian mixture models is performed and McLachlan s bootstrap method is used to test the number of component...

This paper is concerned with estimating a probability density function of human skin color using a finite Gaussian mixture model whose parameters are estimated through the EM algorithm. There are no limitations regarding if person is black or white. Two important sections of Gaussian mixture are parameter estimation and determining the number of mixture components. Experimental results show tha...

1 ABSTRACT We present a discriminant measure that can be used to determine the model complexity in a speech recognition system. In the speech recogition process, given a test feature vector the conditional probability of the feature vector has to be obtained for several al-lophone (sub-phonetic units) classes using a gaussian-mixture density model for each class. The gaussian-mixture models are...

We propose a semiparametric modeling approach for mixtures of symmetric distributions. The mixture model is built from a common symmetric density with different components arising through different location parameters. This structure ensures identifiability for mixture components, which is a key feature of the model as it allows applications to settings where primary interest is inference for t...

Moment closure approximations are used to provide analytic approximations to non-linear stochastic population models. They often provide insights into model behaviour and help validate simulation results. However, existing closure schemes typically fail in situations where the population distribution is highly skewed or extinctions occur. In this study we address these problems by introducing n...

This paper presents parametric probability density models for call holding time (CHT)based on the actual data collected for over a week from the IP based public Emergency Information Network (EIN) in Mongolia. When the set of chosen candidates of Gamma distribution family is fitted to the call holding time data, it is observed that the whole area in the CHT empirical histogram is underestimated...

In many multiple testing procedures, accurate modeling of the p-value distribution is a key issue. Mixture distributions have been shown to provide adequate models for p-value densities under the null and the alternative hypotheses. An important parameter of the mixture model that needs to be estimated is the proportion of true null hypotheses, which under the mixture formulation becomes the pr...