Reversible jump Markov chain Monte Carlo (RJMCMC) is a recent method which makes it possible to construct reversible Markov chain samplers that jump between parameter subspaces of different dimensionality. In this paper, we propose a new RJMCMC sampler for multivariate Gaussian mixture identification and we apply it to color image segmentation. For this purpose, we consider a first order Markov...

Markov chains are commonly used in modeling many practical systems such as queuing systems, manufacturing systems and inventory systems. They are also effective in modeling categorical data sequences. In a conventional nth order multivariate Markov chain model of s chains, and each chain has the same set of m states, the total number of parameters required to set up the model is O(mns). Such hu...

We study estimation in the class of stationary variable length Markov chains (VLMC) on a nite space. The processes in this class are still Markovian of higher order, but with memory of variable length yielding a much bigger and structurally richer class of models than ordinary higher order Markov chains. From a more algo-rithmic view, the VLMC model class has attracted interest in information t...

We present a simple, effective generalisation of variable order Markov models to full online Bayesian estimation. The mechanism used is close to that employed in context tree weighting. The main contribution is the addition of a prior, conditioned on context, on the Markov order. The resulting construction uses a simple recursion and can be updated efficiently. This allows the model to make pre...

Estimation of the parameters of Markov random field models for spatial and temporal data arises in many applications. There are computational and statistical challenges in developing efficient estimators because of the complexity of the joint distribution of the spatio-temporal models, especially when they involve hidden states that also need to be estimated from the observations. We develop co...

The solution of many important signal processing problems depends on the estimation of the parameters of a Hidden Markov Model (HMM). Unfortunately, to date the only known methods for performing this estimation have been iterative, and therefore computationally demanding. By way of contrast, this paper presents a new fast and non-iterative method that utilizes certain recent ‘state spaced subsp...

detention rockfill dams are an easy and common tool for flood control. due to their coarse pores, the flow in void spaces is turbulent and non-darcy. different relationships introduced by researchers are used to define the hydraulics of the flow within the rockfill materials. the present research is aimed at gaining a better understanding of the differ-ence among these relationships and the sou...

  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, ...

An iterative stochastic algorithm to perform maximum a posteriori parameter estimation of hidden Markov models is proposed. It makes the most of the statistical model by introducing an artiicial probability model based on an increasing number of the unobserved Markov chain at each iteration. Under minor regularity assumptions, we provide suucient conditions to ensure global convergence of this ...