期刊论文详细信息
JOURNAL OF APPROXIMATION THEORY 卷:266
Absorbing-reflecting factorizations for birth-death chains on the integers and their Darboux transformations
Article
de la Iglesia, Manuel D.1  Juarez, Claudia2 
[1] Univ Nacl Autonoma Mexico, Inst Matemat, Ciudad De Mexico 04510, Mexico
[2] Univ Nacl Autonoma Mexico, Inst Invest Matemat Aplicadas & Sistemas, Circuito Escolar 3000, Ciudad De Mexico 04510, Mexico
关键词: Birth-death chains;    Matrix factorizations;    Darboux transformations;    Orthogonal polynomials;    Geronimus and Christoffel transformations;   
DOI  :  10.1016/j.jat.2021.105583
来源: Elsevier
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【 摘 要 】

We consider a new way of factorizing the transition probability matrix of a discrete-time birth-death chain on the integers by means of an absorbing and a reflecting birth-death chain to the state 0 and viceversa. First we will consider reflecting-absorbing factorizations of birth-death chains on the integers. We give conditions on the two free parameters such that each of the factors is a stochastic matrix. By inverting the order of the factors (also known as a Darboux transformation) we get new families of almost birth-death chains on the integers with the only difference that we have new probabilities going from the state 1 to the state -1 and viceversa. On the other hand an absorbing-reflecting factorization of birth-death chains on the integers is only possible if both factors are split into two separated birth-death chains at the state 0. Therefore it makes more sense to consider absorbing-reflecting factorizations of almost birth-death chains with extra transitions between the states 1 and -1 and with some conditions. This factorization is now unique and by inverting the order of the factors we get a birth-death chain on the integers. In both cases we identify the spectral matrices associated with the Darboux transformation, the first one being a Geronimus transformation and the second one a Christoffel transformation of the original spectral matrix. We also study the probabilistic implications of both transformations. Finally, we apply our results to examples of chains with constant transition probabilities. (C) 2021 Elsevier Inc. All rights reserved.

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