Multiple Orthogonal Polynomials and Random Walks

Given a non-negative Jacobi matrix describing higher order recurrence relations for multiple orthogonal polynomials of type II and corresponding linear forms of type I, a general strategy for constructing a pair of stochastic matrices, dual to each other, is provided. The corresponding Markov chains (or 1D random walks) allow, in one transition, to reach for the N -th previous states, to remain in the state or reach for the immediately next state. The dual Markov chains allow, in one transition, to reach for the N -th next states, to remain in the state or reach for immediately previous state. The connection between both dual Markov chains is discussed at the light of the Poincaré’s theorem on ratio asymptotics for homogeneous linear recurrence relations and the Christo el–Darboux formula within the sequence of multiple orthogonal polynomials and linear forms of type I. The Karlin–McGregor representation formula is extended to both dual random walks, and applied to the discussion of the corresponding generating functions and first-passage distributions. Recurrent or transient character of the Markov chain is discussed. Steady state and some conjectures on its existence and the relation with mass points are also given. The Jacobi–Piñeiro multiple orthogonal polynomials are taken as a case study of the described results. For the first time in the literature, an explicit formula for the type I Jacobi–Piñeiro polynomials is determined. Then, the region of parameters where the Jacobi matrix is non-negative is given. Moreover, two stochastic matrices, describing two dual random walks, one allowing to reach for the two previous states, remain or reach for the next, and the other allowing to reach for the two next states, remain or reach for the previous, are given in terms of the Jacobi matrix and the values of the multiple orthogonal polynomials of type II and corresponding linear forms of type I at the point 1. The region of parameters where the Markov chains are recurrent or transient is given, and it is conjectured that when recurrent, the Markov chains are null recurrent and, consequently, the expected return times are infinity. Examples of recurrent and transient Jacobi–Piñeiro random walks are constructed explicitly. 2020 Mathematics Subject Classi cation. 42C05,33C45,33C47,60J10,60Gxx.