This paper introduces a mixture model based on the beta distribution, without preestablished means and variances, to analyze a large set of Beauty-Contest data obtained from diverse groups of experiments (Bosch-Domènech et al. 2002). This model gives a better fit of the experimental data, and more precision to the hypothesis that a large proportion of individuals follow a common pattern of reas...

A generative model is, of necessity, a vast simplification of the deeply complex real-world phenomena that govern any observed data set. It is only via this simplification that we can arrive at a tractable data analysis and discover meaningful and actionable patterns in data. In this sense, typically any model of a real-world data set is misspecified, and misspecification is unavoidable. But wh...

We introduce a new technique for calculating the perceived similarity of two songs based on their spectral content. Our method uses a set of hidden Markov Models to model the temporal evolution of a song. We then compute a dissimilarity distance measure based on finding log likelihood probabilities using Monte Carlo sampling. This method is compared to a previously established technique that pe...

Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact, a new numer...

A mixture distribution model is introduced to characterize the source statistics in a frequency domain image coder. More speciically, a Gaussian mixture distribution is shown to be a good statistical model of the probability density function of the composite decomposed signal. Theoretical results based on the mixture distribution model are found that quantify the obtainable entropy rate gain in...

Gaussian Mixture Models (GMM) have been broadly applied for the fitting of probability density function. However, due to the intrinsic linearity of GMM, usually many components are needed to appropriately fit the data distribution, when there are curve manifolds in the data cloud. In order to solve this problem and represent data with curve manifolds better, in this paper we propose a new nonli...

We propose a prior probability model in the wavelet coeecient space. The proposed model implements wavelet coeecient thresholding by full posterior inference in a coherent probability model. We introduce a prior probability model with mixture priors for the wavelet coeecients. The prior includes a positive prior probability mass at zero which leads to a posteriori threshold-ing and generally to...