Large Deviations for a Random Walk Model with State-Dependent Noise

Large Deviations for a Random Walk Model with State-Dependent Noise

In this paper we prove the Laplace principle for a class of random walks with state-dependent noise. The Laplace principle implies a large deviations principle with the same rate function. This type of model has important applications in queueing and communication theory, and in the area of stochastic approximation.

Large Deviations for Random Walk in Random

Averaged Large Deviations for Random Walk in a Random Environment

Abstract. In his 2003 paper, Varadhan proves the averaged large deviation principle (LDP) for the mean velocity of a particle performing random walk in a random environment (RWRE) on Z with d ≥ 1, and gives a variational formula for the corresponding rate function Ia. Under the non-nestling assumption (resp. Kalikow’s condition), we show that Ia is strictly convex and analytic on a non-empty op...

Large Deviations for Random Walk in a Random Environment

In this work, we study the large deviation properties of random walk in a random environment on Z with d ≥ 1. We start with the quenched case, take the point of view of the particle, and prove the large deviation principle (LDP) for the pair empirical measure of the environment Markov chain. By an appropriate contraction, we deduce the quenched LDP for the mean velocity of the particle and obta...

Quenched Large Deviations for Random Walk in a Random Environment

We take the point of view of a particle performing random walk with bounded jumps on Z in a stationary and ergodic random environment. We prove the quenched large deviation principle (LDP) for the pair empirical measure of the environment Markov chain. By an appropriate contraction, we deduce the quenched LDP for the mean velocity of the particle and obtain a variational formula for the corresp...