Gaussian mixture vector autoregression

Continuous time Gaussian Autoregression

The problem of tting continuous time autoregressions linear and non linear to closely and regularly spaced data is considered For the linear case Jones and Bergstrom used state space representations to compute exact maximum likelihood estimators and Phillips did so by tting an appropriate discrete time ARMA process to the data In this paper we use exact conditional maximum likelihood estimators...

Lecture 6 : Vector Autoregression ∗

­­Image Segmentation using Gaussian Mixture Model

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,...

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, ...

Medical Image Compression Using Vector Quantization and Gaussian Mixture Model

Codebook design for vector quantization could be performed using clustering technique. The Gaussian Mixture Modeling (GMM) clustering algorithm involves modeling a statistical distribution by a mixture (or weighted sum) of other distributions. GMM has proven superior efficiency in both time and accuracy and has been used with vector quantization in some applications. This paper introduces a med...