Hypergeometric Multiple Orthogonal Polynomials and Random Walks

TL;DR

This paper establishes that certain hypergeometric multiple orthogonal polynomials are associated with complex Markov chains beyond birth-death processes, providing their stochastic matrices, recurrence relations, and conditions for recurrence or transience.

Contribution

It demonstrates the stochastic nature of hypergeometric multiple orthogonal polynomials and introduces new Markov chains with transitions beyond traditional birth-death models, including their factorizations and transformations.

Findings

Identification of hypergeometric multiple orthogonal polynomials as random walk polynomials.

Construction of stochastic matrices dual to each other for these polynomials.

Analysis of recurrence, transience, and stochastic factorizations for the associated Markov chains.

Abstract

The recently found hypergeometric multiple orthogonal polynomials on the step-line by Lima and Loureiro are shown to be random walk polynomials. It is proven that the corresponding Jacobi matrix and its transpose, which are nonnegative matrices and describe higher recurrence relations, can be normalized to two stochastic matrices, dual to each other. Using the Christoffel-Darboux formula on the step-line and the Poincar\'e theory for non-homogeneous recurrence relations it is proven that both stochastic matrices are related by transposition in the large $n$ limit. These random walks are beyond birth and death, as they describe a chain in where transitions to the two previous states are allowed, or in the dual to the two next states.The corresponding Karlin-McGregor representation formula is given for these new Markov chains. The regions of hypergeometric parameters where the Markov chains are recurrent or transient are given. Stochastic factorizations, in terms of pure birth and of pure death factors, for the corresponding Markov matrices of types I and II, are provided.Twelve uniform Jacobi matrices and the corresponding random walks, related to a Jacobi matrix of Toeplitz type, and theirs stochastic or semi-stochastic matrices (with sinks and sources), that describe Markov chains beyond birth and death, are found and studied. One of these uniform stochastic cases, which is a recurrent random walk, is the only hypergeometric multiple random walk having a uniform stochastic factorization. The corresponding weights, Jacobi and Markov transition matrices and sequences of type II multiple orthogonal polynomials are provided. Chain of Christoffel transformations connecting the stochastic uniform tuples between them, and the semi-stochastic uniform tuples, between them, are presented.