Ergodicity of nonlinear Markov operators on the finite dimensional space

A nonlinear Markov chain is a discrete time stochastic process whose transition matrices may depend not only on the current state of the process but also on the current distribution of the process. In this paper, we study strong and uniform ergodicity of nonlinear Markov operators defined by stocha...

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Main Author: Saburov, Mansoor
Format: Article
Language:English
English
English
Published: Elsevier BV 2016
Subjects:
Online Access:http://irep.iium.edu.my/51167/
http://irep.iium.edu.my/51167/1/Nonlinear_Markov_Operators_---_NA.pdf
http://irep.iium.edu.my/51167/4/51167_Ergodicity%20of%20nonlinear%20Markov_Scopus.pdf
http://irep.iium.edu.my/51167/10/51167_Ergodicity%20of%20nonlinear%20Markov_WOS.pdf
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author Saburov, Mansoor
author_facet Saburov, Mansoor
author_sort Saburov, Mansoor
building IIUM Repository
collection Online Access
description A nonlinear Markov chain is a discrete time stochastic process whose transition matrices may depend not only on the current state of the process but also on the current distribution of the process. In this paper, we study strong and uniform ergodicity of nonlinear Markov operators defined by stochastic hypermatrices (higher order matrix). We introduce Dobrushin’s ergodicity coefficient for a stochastic hypermatrix which enables to study ergodicity of nonlinear Markov operators. By introducing a notion of scrambling stochastic hypermatrix, we study the strong ergodicity of scrambling nonlinear Markov operators.
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institution International Islamic University Malaysia
institution_category Local University
language English
English
English
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publishDate 2016
publisher Elsevier BV
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spelling iium-511672017-04-13T07:33:56Z http://irep.iium.edu.my/51167/ Ergodicity of nonlinear Markov operators on the finite dimensional space Saburov, Mansoor QA Mathematics A nonlinear Markov chain is a discrete time stochastic process whose transition matrices may depend not only on the current state of the process but also on the current distribution of the process. In this paper, we study strong and uniform ergodicity of nonlinear Markov operators defined by stochastic hypermatrices (higher order matrix). We introduce Dobrushin’s ergodicity coefficient for a stochastic hypermatrix which enables to study ergodicity of nonlinear Markov operators. By introducing a notion of scrambling stochastic hypermatrix, we study the strong ergodicity of scrambling nonlinear Markov operators. Elsevier BV 2016-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/51167/1/Nonlinear_Markov_Operators_---_NA.pdf application/pdf en http://irep.iium.edu.my/51167/4/51167_Ergodicity%20of%20nonlinear%20Markov_Scopus.pdf application/pdf en http://irep.iium.edu.my/51167/10/51167_Ergodicity%20of%20nonlinear%20Markov_WOS.pdf Saburov, Mansoor (2016) Ergodicity of nonlinear Markov operators on the finite dimensional space. Nonlinear Analysis: Theory, Methods and Applications, 143. pp. 105-119. ISSN 0362-546X (In Press) http://www.sciencedirect.com/science/article/pii/S0362546X16300712 10.1016/j.na.2016.05.006
spellingShingle QA Mathematics
Saburov, Mansoor
Ergodicity of nonlinear Markov operators on the finite dimensional space
title Ergodicity of nonlinear Markov operators on the finite dimensional space
title_full Ergodicity of nonlinear Markov operators on the finite dimensional space
title_fullStr Ergodicity of nonlinear Markov operators on the finite dimensional space
title_full_unstemmed Ergodicity of nonlinear Markov operators on the finite dimensional space
title_short Ergodicity of nonlinear Markov operators on the finite dimensional space
title_sort ergodicity of nonlinear markov operators on the finite dimensional space
topic QA Mathematics
url http://irep.iium.edu.my/51167/
http://irep.iium.edu.my/51167/
http://irep.iium.edu.my/51167/
http://irep.iium.edu.my/51167/1/Nonlinear_Markov_Operators_---_NA.pdf
http://irep.iium.edu.my/51167/4/51167_Ergodicity%20of%20nonlinear%20Markov_Scopus.pdf
http://irep.iium.edu.my/51167/10/51167_Ergodicity%20of%20nonlinear%20Markov_WOS.pdf