Abstract: 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,...

  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 have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...

background & aim: esophageal cancer is one of the main common causes of death. the high prevalence of esophageal cancer in northern iran is an important public health problem. the main aim of this paper was to assess the factors affecting survival of  atients with esophageal cancer in neighbor provinces around the caspian sea using weibull mixture cure model and mixture cure model based on a no...

The incorporation of the Random Vector Functional Link (RVFL) concept into mixture models for predicting conditional probability densities achieves a considerable speed-up of the training process. This allows the creation of a large ensemble of predictors, which results in an improvement in the generalization performance .

In this paper, we investigate questions involving the impact of a multitude of covariates and their interactions on the scores of a comprehensive exit exam from 121 undergraduate students in Texas State University via a hierarchical Bayesian mixture model. The model uses a mixture of Beta distributions, inflated for the purposes of modeling the behavior of students who are no-shows for the exam...

the aim of this study is to introduce a parametric mixture model to analysis the competing-risks data with two types of failure. in mixture context, i t h type of failure is i th component. the baseline failure time for the first and second types of failure are modeled as proportional hazard models according to weibull and gompertz distributions, respectively. the covariates affect on both the ...