Please use this identifier to cite or link to this item: doi:10.22028/D291-31058
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Title: Approximate adaptive uniformization of continuous-time Markov chains
Author(s): Andreychenko, Alexander
Sandmann, Werner
Wolf, Verena
Language: English
Title: Applied mathematical modelling : simulation and computation for engineering and environmental systems
Volume: 61
Startpage: 561
Endpage: 576
Publisher/Platform: Elsevier
Year of Publication: 2018
Publikation type: Journal Article
Abstract: We consider the approximation of transient (time dependent) probability distributions of discrete-state continuous-time Markov chains on large, possibly infinite state spaces. A framework for approximate adaptive uniformization is provided, which generalizes the well-known uniformization technique and many of its variants. Based on a birth process and a discrete-time Markov chain a computationally tractable approximating process/model is constructed. We investigate the theoretical properties of this process and prove that it yields computable lower and upper bounds for the desired transient probabilities. Finally, we discuss different specific ways of performing approximate adaptive uniformization and analyze the corresponding approximation errors. The application is illustrated by an example of a stochastic epidemic model.
DOI of the first publication: 10.1016/j.apm.2018.05.009
URL of the first publication:
Link to this record: hdl:20.500.11880/29200
ISSN: 0307-904x
Date of registration: 28-May-2020
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Professorship: MI - Prof. Dr. Verena Wolf
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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