Comparing the Intercept Mixture Model with the Slack-Variable Mixture Model

Comparing the Intercept Mixture Model with the Slack-Variable Mixture Model Comparación del modelo de mezclas con intercepto con el modelo de mezclas de variable de holgura

Mixture experiments are experiments performed using ingredients whose proportions are restricted. This restriction may result in extremely small range in terms of the mixtures, causing difficulties in model fitting arising from ill-conditioning. The choice of model form is a very important factor in the numerical stability of the information matrix. In this paper, the intercept model is compare...

Comparing the Intercept Mixture Model with the Slack-Variable Mixture Model Comparación del modelo de mezclas con intercepto con el modelo de mezclas de variable de holgura

Mixture experiments are experiments performed using ingredients whose proportions are restricted. This restriction may result in extremely small range in terms of the mixtures, causing difficulties in model fitting arising from ill-conditioning. The choice of model form is a very important factor in the numerical stability of the information matrix. In this paper, the intercept model is compare...

Sampling the Dirichlet Mixture Model with Slices

IMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL

  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, ...

The Infinite Gaussian Mixture Model

In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the “right” number of mixture components. Inference in the model is done using an efficient parameter-free Markov Chain that relies entirely on Gibbs sampling.