Marche aléatoire sur le semi-groupe des contractions de . Cas de la marche aléatoire sur avec choc élastique en zéro
Marche aléatoire sur le semi-groupe des contractions de . Cas de la marche aléatoire sur avec choc élastique en zéro
Marches aléatoires auto-évitantes et mesures de polymères
Marches aléatoires récurrentes sur les demi-groupes localement compacts
Marches aléatoires sur les espaces homogènes
Marches aléatoires sur les espaces homogènes des groupes nilpotents connexes à génération compacte
Marches aléatoires sur les groupes non abéliens
Marches aléatoires sur un demi-groupe compact
Marches au hasard sur les demi-groupes discrets, I
Marches au hasard sur les demi-groupes discrets, II
Marches de Bernoulli quantiques
Marches de Harris sur les groupes localement compacts. II
Marches de Harris sur les groupes localement compacts V
Marches en milieu aléatoire et mesures quasi-invariantes pour un système dynamique
The invariant measures for a Markovian operator corresponding to a random walk, in a random stationary one-dimensional environment defined by a dynamical system, are quasi-invariant measures for the system. We discuss the construction of such measures in the general case and show unicity, under some assumptions, for a rotation on the circle.
Marches récurrentes au sens de Harris sur les groupes localement compacts. I
Marginal problem, statistical estimation, and Möbius formula
A solution to the marginal problem is obtained in a form of parametric exponential (Gibbs–Markov) distribution, where the unknown parameters are obtained by an optimization procedure that agrees with the maximum likelihood (ML) estimate. With respect to a difficult performance of the method we propose also an alternative approach, providing the original basis of marginals can be appropriately extended. Then the (numerically feasible) solution can be obtained either by the maximum pseudo-likelihood...
Markov chain intersections and the loop-erased walk
Markov chains approximation of jump–diffusion stochastic master equations
Quantum trajectories are solutions of stochastic differential equations obtained when describing the random phenomena associated to quantum continuous measurement of open quantum system. These equations, also called Belavkin equations or Stochastic Master equations, are usually of two different types: diffusive and of Poisson-type. In this article, we consider more advanced models in which jump–diffusion equations appear. These equations are obtained as a continuous time limit of martingale problems...
Markov processes and convex minorants
Markov Property of Generalized Fields and Axiomatic Potential Theory.