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dc.contributor.authorDayar, T.en_US
dc.contributor.authorSandmann W.en_US
dc.contributor.authorSpieler, D.en_US
dc.contributor.authorWolf V.en_US
dc.date.accessioned2016-02-08T09:49:31Z
dc.date.available2016-02-08T09:49:31Z
dc.date.issued2011en_US
dc.identifier.issn18678en_US
dc.identifier.urihttp://hdl.handle.net/11693/21667
dc.description.abstractSystems of stochastic chemical kinetics are modeled as infinite level-dependent quasibirth-and-death (LDQBD) processes. For these systems, in contrast to many other applications, levels have an increasing number of states as the level number increases and the probability mass may reside arbitrarily far away from lower levels. Ideas from Lyapunov theory are combined with existing matrix-analytic formulations to obtain accurate approximations to the stationary probability distribution when the infinite LDQBD process is ergodic. Results of numerical experiments on a set of problems are provided. © Applied Probability Trust 2011.en_US
dc.language.isoEnglishen_US
dc.source.titleAdvances in Applied Probabilityen_US
dc.relation.isversionofhttp://dx.doi.org/10.1239/aap/1324045696en_US
dc.subjectLevel-dependent quasi-birth-and-death processen_US
dc.subjectLyapunov bounden_US
dc.subjectMatrix-analytic solutionen_US
dc.subjectState space truncationen_US
dc.subjectStochastic chemical kineticsen_US
dc.subjectErgodicsen_US
dc.subjectLevel-dependent quasi-birth-and-death processen_US
dc.subjectLyapunoven_US
dc.subjectLyapunov theoriesen_US
dc.subjectNumber of stateen_US
dc.subjectNumerical experimentsen_US
dc.subjectOther applicationsen_US
dc.subjectQBD processen_US
dc.subjectState spaceen_US
dc.subjectKineticsen_US
dc.subjectMatrix algebraen_US
dc.subjectProbability distributionsen_US
dc.subjectReaction kineticsen_US
dc.subjectStochastic systemsen_US
dc.subjectChemical analysisen_US
dc.titleInfinite level-dependent QBD processes and matrix-analytic solutions for stochastic chemical kineticsen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineering, Bilkent University, TR-06800 Bilkent, Ankara, Turkeyen_US
dc.departmentDepartment of Applied Stochastics and Operations Research, Clausthal University of Technology, Erzstr. 1, D-38678 Clausthal-Zellerfeld, Germanyen_US
dc.departmentFaculty of Computer Science, Saarland University, D-66123 Saarbrücken, Germanyen_US
dc.citation.spage1005en_US
dc.citation.epage1026en_US
dc.citation.volumeNumber43en_US
dc.citation.issueNumber4en_US
dc.identifier.doi10.1239/aap/1324045696en_US


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