A Large Deviation Principle for the Stochastic Heat Equation with General Rough Noise

Large Deviation Principle for General Occupancy Models

We use process level large deviation analysis to obtain the rate function for a general family of occupancy problems. Our interest is the asymptotics of the empirical distributions of various quantities (such as the fraction of urns that contain a given number of balls). In the general setting, balls are allowed to land in a given urn depending on the urn’s contents prior to the throw. We discu...

Stochastic heat equation with white-noise drift

Stochastic Sub - Additivity Approach to the Conditional Large Deviation Principle

University of Chicago Given two Polish spaces AX and AY, let ρ AX × AY → d be a bounded measurable function. Let X = Xn n ≥ 1 and Y = Yn n ≥ 1 be two independent stationary processes on AX and A ∞ Y , respectively. The article studies the large deviation principle (LDP) for n−1 ∑n k=1 ρ Xk Yk , conditional on X. Based on a stochastic version of approximate subadditivity, it is shown that if Y s...

A large deviation principle with queueing applications

In this paper, we present a large deviation principle for partial sums processes indexed by the half line, which is particularly suited to queueing applications. The large deviation principle is established in a topology that is finer than the topology of uniform convergence on compacts and in which the queueing map is continuous. Consequently, a large deviation principle for steady-state queue...

A large deviation principle for Dirichlet posteriors

Let Xk be a sequence of independent and identically distributed random variables taking values in a compact metric space Ω, and consider the problem of estimating the law of X1 in a Bayesian framework. A conjugate family of priors for non-parametric Bayesian inference is the Dirichlet process priors popularized by Ferguson. We prove that if the prior distribution is Dirichlet, then the sequence...