Strong stochastic stability and rate of mixing for unimodal maps
Subdominant eigenvalues for stochastic matrices with given column sums.
Substochastic transition operators on trees and their associated Poisson integrals
Suites de Fibonacci généralisées et Chaínes de Markov.
Sur la démonstration des formules en théorie discrète du potentiel
Sur le nombre de points visités par une marche aléatoire sur un amas infini de percolation
On s’intéresse à une marche aléatoire simple sur un amas infini issu d’un processus de percolation surcritique sur les arêtes de de loi . On montre que la transformée de Laplace du nombre de points visités au temps , noté , a un comportement similaire au cas où la marche évolue dans . Plus précisément, on établit que pour tout , il existe des constantes , telles que pour presque toute réalisation de la percolation telle que l’origine appartienne à l’amas infini et pour assez grand,Le...
Sur le schéma de remplissage pour les processus récurrents
Sur les lois d'entrée et de sortie d'une chaîne de Markov
Sur les processus à valeurs dans le cube fondamental de Hilbert
Sur l'irréductibilité des chaînes de Markov
Tail probability and singularity of Laplace-Stieltjes transform of a Pareto type random variable
We give a sufficient condition for a non-negative random variable to be of Pareto type by investigating the Laplace-Stieltjes transform of the cumulative distribution function. We focus on the relation between the singularity at the real point of the axis of convergence and the asymptotic decay of the tail probability. For the proof of our theorems, we apply Graham-Vaaler’s complex Tauberian theorem. As an application of our theorems, we consider the asymptotic decay of the stationary distribution...
Tesselation of a triangle by repeated barycentric subdivision.
The behavior of a Markov network with respect to an absorbing class: the target algorithm
In this paper, we face a generalization of the problem of finding the distribution of how long it takes to reach a “target” set T of states in Markov chain. The graph problems of finding the number of paths that go from a state to a target set and of finding the n-length path connections are shown to belong to this generalization. This paper explores how the state space of the Markov chain can be reduced by collapsing together those states that behave in the same way for the purposes of calculating...
The central limit theorem for Markov chains with the simultatneous total variation convergence of the chain.
The compositional construction of Markov processes II
We add sequential operations to the categorical algebra of weighted and Markov automata introduced in [L. de Francesco Albasini, N. Sabadini and R.F.C. Walters, arXiv:0909.4136]. The extra expressiveness of the algebra permits the description of hierarchical systems, and ones with evolving geometry. We make a comparison with the probabilistic automata of Lynch et al. [SIAM J. Comput. 37 (2007) 977–1013].
The compositional construction of Markov processes II
We add sequential operations to the categorical algebra of weighted and Markov automata introduced in [L. de Francesco Albasini, N. Sabadini and R.F.C. Walters, arXiv:0909.4136]. The extra expressiveness of the algebra permits the description of hierarchical systems, and ones with evolving geometry. We make a comparison with the probabilistic automata of Lynch et al. [SIAM J. Comput.37 (2007) 977–1013].
The computation of stationary distributions of Markov chains through perturbations.
The cutoff phenomenon for ergodic Markov processes.
The dynamics of mutation-selection algorithms with large population sizes
The Dyson Brownian Minor Process
Consider an Hermitean matrix valued stochastic process where the elements evolve according to Ornstein-Uhlenbeck processes. It is well known that the eigenvalues perform a so called Dyson Brownian motion, that is they behave as Ornstein-Uhlenbeck processes conditioned never to intersect.In this paper we study not only the eigenvalues of the full matrix, but also the eigenvalues of all the principal minors. That is, the eigenvalues of the minors in the upper left corner of . Projecting this...