the notion of n-ple markov process is defined in a quite general framework and it is shown that n-ple markov processes-arel inear combinationso f some martingales

This paper presents the analysis of a renewal input  finite buffer queue wherein the customers can decide either to  join the queue with a probability or balk. The service process is Markovian service process ($MSP$) governed  by an underlying $m$-state Markov chain. Employing the supplementary  variable and imbedded Markov chain techniques,   the steady-state system length distributions at pre...

This work considers the problem of Bayesian estimation of a hidden Markov source corrupted by additive noise. We develop sequential and complete sequence Bayesian de-coders for noisy sources with memory and apply them to the log-area ratio (LAR) coeecients of speech corrupted by additive white Gaussian noise. To this end, we follow a model-based approach in which the source is approximated by a...

The convergence of additive and multiplicative Schwarz methods for computing certain characteristics of Markov chains such as stationary probability vectors and mean first passage matrices is studied. Our main result is a convergence theorem for multiplicative Schwarz iterations when applied to singular systems. As a by-product we also obtain a convergence result for alternating iterations. It ...

suitability analysis is a prerequisite for sustainable agricultural production and it involves evaluation of the environmental parameters. the development and creation of appropriate points for this land use without considering environmental capability will result in the appearance of several ecological, economic, and social problems. the multi-criteria decision making (mcdm) models were used f...

The recently proposed additive noise model has advantages over previous directed structure learning approaches since it (i) does not assume linearity or Gaussianity and (ii) can discover a unique DAG rather than its Markov equivalence class. However, for certain distributions, e.g. linear Gaussians, the additive noise model is invertible and thus not useful for structure learning, and it was or...