On the limiting probability distribution of a transition probability tensor

Wen Li, Kwok Po NG

Research output: Contribution to journalArticlepeer-review

96 Citations (Scopus)

Abstract

In this paper, we propose and develop an iterative method to calculate a limiting probability distribution vector of a transition probability tensor arising P from a higher order Markov chain. In the model, the computation of such limiting probability distribution vector x can be formulated as a Z-eigenvalue problem Pxm-1 = x associated with the eigenvalue 1 of P where all the entries of x are required to be non-negative and its summation must be equal to one. This is an analog of the matrix case for a limiting probability vector of a transition probability matrix arising from the first-order Markov chain. We show that if is a transition probability tensor, then solutions of this Z-eigenvalue problem exist. When P is irreducible, all the entries of solutions are positie. With some suitable conditions of P, the limiting probability distribution vector is even unique. Under the same uniqueness assumption, the linear convergence of the iterative method can be established. Numerical examples are presented to illustrate the theoretical results of the proposed model and the iterative method.

Original languageEnglish
Pages (from-to)362-385
Number of pages24
JournalLinear and Multilinear Algebra
Volume62
Issue number3
DOIs
Publication statusPublished - Mar 2014

Scopus Subject Areas

  • Algebra and Number Theory

User-Defined Keywords

  • higher-order Markov chains
  • it Z-eigenvalue
  • iterative method
  • limiting probability distribution vector
  • non-negative tensor
  • transition probability tensor

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