Désintégration des fonctionnelles de Markov
Detecting a local perturbation in a continuous scenery.
Determinantal transition kernels for some interacting particles on the line
We find the transition kernels for four markovian interacting particle systems on the line, by proving that each of these kernels is intertwined with a Karlin–McGregor-type kernel. The resulting kernels all inherit the determinantal structure from the Karlin–McGregor formula, and have a similar form to Schütz’s kernel for the totally asymmetric simple exclusion process.
Deterministic Chaos vs. Stochastic Fluctuation in an Eco-epidemic Model
An eco-epidemiological model of susceptible Tilapia fish, infected Tilapia fish and Pelicans is investigated by several author based upon the work initiated by Chattopadhyay and Bairagi (Ecol. Model., 136, 103–112, 2001). In this paper, we investigate the dynamics of the same model by considering different parameters involved with the model as bifurcation parameters in details. Considering the intrinsic growth rate of susceptible Tilapia fish as bifurcation parameter, we demonstrate the period doubling...
Deterministic optimal policies for Markov control processes with pathwise constraints
This paper deals with discrete-time Markov control processes in Borel spaces with unbounded rewards. Under suitable hypotheses, we show that a randomized stationary policy is optimal for a certain expected constrained problem (ECP) if and only if it is optimal for the corresponding pathwise constrained problem (pathwise CP). Moreover, we show that a certain parametric family of unconstrained optimality equations yields convergence properties that lead to an approximation scheme which allows us to...
Deux propriétés des ensembles minces (abstraits)
Développement des distributions suivant les chaos de Wiener et applications à l'analyse stochastique
Deviation bounds for additive functionals of Markov processes
In this paper we derive non asymptotic deviation bounds forwhere is a stationary and ergodic Markov process and is some integrable function. These bounds are obtained under various moments assumptions for , and various regularity assumptions for . Regularity means here that may satisfy various functional inequalities (F-Sobolev, generalized Poincaré etc.).
deviation bounds for additive functionals of markov processes
In this paper we derive non asymptotic deviation bounds for where X is a μ stationary and ergodic Markov process and V is some μ integrable function. These bounds are obtained under various moments assumptions for V, and various regularity assumptions for μ. Regularity means here that μ may satisfy various functional inequalities (F-Sobolev, generalized Poincaré etc.).
Die Klassifikation der Zustände bei inhomogenen Markoffschen Ketten.
Die Schätzung des linearen Todesprozesses bei fehlerbehafteten Beobachtungen.
Différentiabilité des fonctions finement harmoniques.
Différentiabilité fine et différentiabilité sur des compacts
Differentiability of excessive functions of one-dimensional diffusions and the principle of smooth fit
The principle of smooth fit is probably the most used tool to find solutions to optimal stopping problems of one-dimensional diffusions. It is important, e.g., in financial mathematical applications to understand in which kind of models and problems smooth fit can fail. In this paper we connect-in case of one-dimensional diffusions-the validity of smooth fit and the differentiability of excessive functions. The basic tool to derive the results is the representation theory of excessive functions;...
Differentiability of multiplicative processes related to branching random walks
Differentiability of stochastic flow of reflected Brownian motions.
Differential equation approximations for Markov chains.
Differential operators and spectral distributions of invariant ensembles from the classical orthogonal polynomials. The continuous case.
Diffusion approximation for a controlled service system
Diffusion approximation for first overflow time in system with finite capacity.