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 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...

In this paper an estimator for speech enhancement based on Laplacian Mixture Model has been proposed. The proposed method, estimates the complex DFT coefficients of clean speech from noisy speech using the MMSE  estimator, when the clean speech DFT coefficients are supposed mixture of Laplacians and the DFT coefficients of  noise are assumed zero-mean Gaussian distribution. Furthermore, the MMS...

We develop in this paper a novel portfolio selection framework with a feature of double robustness in both return distribution modeling and portfolio optimization. While predicting the future return distributions always represents the most compelling challenge in investment, any underlying distribution can be always well approximated by utilizing a mixture distribution, if we are able to ensure...

This paper presents a system for audio classification using multiple binary ensemble classifiers with music tag based indexing in a one-versus-all classification scenario. The proposed system has won the audio classification task on mood dataset in MIREX 2010 and is implemented as follows. First, in the training phase, the frame-based 70-dimensional feature vectors are extracted from a training...

The main goal of this paper is to describe inference in hybrid Bayesian networks (BNs) using mixture of polynomials (MOP) approximations of probability density functions (PDFs). Hybrid BNs contain a mix of discrete, continuous, and conditionally deterministic random variables. The conditionals for continuous variables are typically described by conditional PDFs. A major hurdle in making inferen...

In this research, we consider an application of the Bayesian Inferences in machine replacement problem. The application is concerned with the time to replace two machines producing a specific product; each machine doing a special operation on the product when there are manufacturing defects because of failures. A common practice for this kind of problem is to fit a single distribution to the co...

The Generalized Pareto Distribution is a very useful tool for modeling in many areas of economics, finance and insurance. The Generalized Pareto Distribution is commonly used for extreme value problems. Especially, the values which exceed the finite threshold, is the focus in extreme value problems like in insurance sector. The Generalized Pareto Distribution is well approach for modeling the s...

Bayesian methods are used to analyse the problem of training a model to make predictions about the probability distribution of data that has yet to be received. Mixture distributions emerge naturally from this framework, but are not well-matched to highdimensional problems such as image processing. An extension, called a partitioned mixture distribution (PMD) is presented, which is essentially ...