Wind modelling with nested Markov chains

Modelling Sleep Stages With Markov Chains

Texture modelling with nested high-order Markov-Gibbs random fields

Currently, Markov–Gibbs random field (MGRF) image models which include high-order interactions are almost always built by modelling responses of a stack of local linear filters. Actual interaction structure is specified implicitly by the filter coefficients. In contrast, we learn an explicit high-order MGRF structure by considering the learning process in terms of general exponential family dis...

Using Markov chains for modelling networks

The paper contains the review and the discussion on modelling communication networks with the use of queuing models and Markov chains. It shows how to take into account various characteristics of real systems – like some control mechanisms and the traffic self-similarity. There are presented two mechanisms modelled with Markov chains: the RED algorithm in TCP/IP and a self-similar traffic shaping.

Software reliability modelling and prediction with hidden Markov chains

The purpose of this paper is to use the framework of hidden Markov chains for the modelling of the failure and debugging process of software, and the prediction of software reliability. The model parameters are estimated using the forward-backward EM algorithm and model selection is done with the BIC criterion. The advantages and drawbacks of this approach with respect to usual modelling are an...

A framework for modelling stochastic optimisation algorithms with Markov chains