Empirical Measure Large Deviations for Reinforced Chains on Finite Spaces

TL;DR

This paper establishes a large deviation principle for empirical measures of reinforced chains on finite spaces, revealing a novel rate function linked to a deterministic control problem with discounted costs.

Contribution

It introduces a new large deviation principle for reinforced chains with a distinctive rate function different from classical Markov chain results.

Findings

Large deviation principle for reinforced chains established.

Rate function characterized by a deterministic control problem.

Different from Donsker-Varadhan rate function.

Abstract

Let $A$ be a transition probability kernel on a finite state space $\Delta^o =\{1, \ldots , d\}$ such that $A(x,y)>0$ for all $x,y \in \Delta^o$. Consider a reinforced chain given as a sequence $\{X_n, \; n \in \mathbb{N}_0\}$ of $\Delta^o$-valued random variables, defined recursively according to, $$L^n = \frac{1}{n}\sum_{i=0}^{n-1} \delta_{X_i}, \;\; P(X_{n+1} \in \cdot \mid X_0, \ldots, X_n) = L^n A(\cdot).$$ We establish a large deviation principle for $\{L^n\}$. The rate function takes a strikingly different form than the Donsker-Varadhan rate function associated with the empirical measure of the Markov chain with transition kernel $A$ and is described in terms of a novel deterministic infinite horizon discounted cost control problem with an associated linear controlled dynamics and a nonlinear running cost involving the relative entropy function. Proofs are based on an analysis of time-reversal of controlled dynamics in representations for log-transforms of exponential moments, and on weak convergence methods.