Calculating principal eigen-functions of non-negative integral kernels: particle approximations and applications

Abstract

Often in applications such as rare events estimation or optimal control it is required that one calculates the principal eigen-function and eigen-value of a non-negative integral kernel. Except in the finite-dimensional case, usually neither the principal eigen-function nor the eigen-value can be computed exactly. In this paper, we develop numerical approximations for these quantities. We show how a generic interacting particle algorithm can be used to deliver numerical approximations of the eigen-quantities and the associated so-called "twisted" Markov kernel as well as how these approximations are relevant to the aforementioned applications. In addition, we study a collection of random integral operators underlying the algorithm, address some of their mean and path-wise properties, and obtain $L_r$ error estimates. Finally, numerical examples are provided in the context of importance sampling for computing tail probabilities of Markov chains and computing value functions for a class of stochastic optimal control problems.

38 pages, 4 figures, 1 table; to appear in Mathematics of Operations Research

References in corpus (3)

• Sequential Monte Carlo smoothing for general state space hidden Markov models
• Dynamic importance sampling for uniformly recurrent markov chains
• Linear Variance Bounds for Particle Approximations of Time-Homogeneous Feynman-Kac Formulae