Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification

Wavelet-Based Bayesian Denoising Using Bernoulli-Gaussian Mixture Model

In general, wavelet coefficients are composed of a few large coefficients and a lot of small ones. Therefore, each wavelet coefficient is efficiently modeled as a random variable of a Bernoulli-Gaussian mixture distribution with unknown parameters. The Bernoulli-Gaussian mixture is composed of the multiplication of the Bernoulli random variable and the Gaussian mixture random variable. In this ...

Bernoulli Mixture Models for Markov Blanket Filtering and Classification

This paper presents the use of Bernoulli mixture models for Markov blanket filtering and classification of binary data. Bernoulli mixture models can be seen as a tool for partitioning an n-dimensional hypercube, identifying regions of high data density on the corners of the hypercube. Once Bernoulli mixture models are computed from a training dataset we use them for determining the Markov blank...

On the use of Bernoulli Mixture Models for Text Classification

‎A Bayesian mixture model‎ for classification of certain and uncertain data

‎There are different types of classification methods for classifying the certain data‎. ‎All the time the value of the variables is not certain and they may belong to the interval that is called uncertain data‎. ‎In recent years‎, ‎by assuming the distribution of the uncertain data is normal‎, ‎there are several estimation for the mean and variance of this distribution‎. ‎In this paper‎, ‎we co...

Bayesian Regression and Classification

In recent years Bayesian methods have become widespread in many domains including computer vision, signal processing, information retrieval and genome data analysis. The availability of fast computers allows the required computations to be performed in reasonable time, and thereby makes the benefits of a Bayesian treatment accessible to an ever broadening range of applications. In this tutorial...