The tail structure of nonhomogeneous finite state Markov chains: survey
Théorème local pour chaînes de Markov de probabilité de transition quasi-compacte. Applications aux chaînes V-géométriquement ergodiques et aux modèles itératifs
Théorèmes limites pour une classe de chaînes de Markov et applications aux difféomorphismes d'Anosov
Theoretical analysis of steady state genetic algorithms
Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are probabilistic algorithms for optimization, which mimic operators from natural selection and genetics. The paper analyses the convergence of the heuristic associated to a special type of Genetic Algorithm, namely the Steady State Genetic Algorithm (SSGA), considered as a discrete-time dynamical system non-generational model. Inspired by the Markov chain results in finite Evolutionary Algorithms, conditions are...
Théorie spectrale d'un opérateur de transition sur un espace métrique compact
Three-dimensional reflected driftless random walks in troughs : new asymptotic behavior
Transforming stochastic matrices for stochastic comparison with the st-order
We present a transformation for stochastic matrices and analyze the effects of using it in stochastic comparison with the strong stochastic (st) order. We show that unless the given stochastic matrix is row diagonally dominant, the transformed matrix provides better st bounds on the steady state probability distribution.
Transforming stochastic matrices for stochastic comparison with the st-order
We present a transformation for stochastic matrices and analyze the effects of using it in stochastic comparison with the strong stochastic (st) order. We show that unless the given stochastic matrix is row diagonally dominant, the transformed matrix provides better st bounds on the steady state probability distribution.
Transient analysis in discrete time of Markovian queues with quadratic rates.
Transient and stationary characteristics of a packet buffer modelled as an map/sm/1/b system
Transient random walk in with stationary orientations
In this paper, we extend a result of Campanino and Pétritis [Markov Process. Relat. Fields 9 (2003) 391–412]. We study a random walk in with random orientations. We suppose that the orientation of the kth floor is given by , where is a stationary sequence of random variables. Once the environment fixed, the random walk can go either up or down or can stay in the present floor (but moving with respect to its orientation). This model was introduced by Campanino and Pétritis in [Markov Process....
Transition probability estimates for reversible Markov chains.
Two algorithms based on Markov chains and their application to recognition of protein coding genes in prokaryotic genomes
Methods based on the theory of Markov chains are most commonly used in the recognition of protein coding sequences. However, they require big learning sets to fill up all elements in transition probability matrices describing dependence between nucleotides in the analyzed sequences. Moreover, gene prediction is strongly influenced by the nucleotide bias measured by e.g. G+C content. In this paper we compare two methods: (i) the classical GeneMark algorithm, which uses a three-periodic non-homogeneous...
Two contributions to the theory of coefficients of ergodicity