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Risk filtering and risk-averse control of partially observable Markov jump processes
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Yan, Ruofan. Risk filtering and risk-averse control of partially observable Markov jump processes. Retrieved from https://doi.org/doi:10.7282/T3D221VW

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TitleRisk filtering and risk-averse control of partially observable Markov jump processes
NameYan, Ruofan (author); Feehan, Paul (chair); Ruszczynski, Andrzej (internal member); Ocone, Daniel (internal member); Dentcheva, Darinka (outside member); Rutgers University; School of Graduate Studies
Date Created2018
Other Date2018-01 (degree)
SubjectMathematics, Risk--Measurement
Extent1 online resource (vii, 61 p.)
DescriptionIn this dissertation, we provide a theory of time-consistent dynamic risk measures for partially observable Markov jump processes in continuous time. By introducing risk filters, which are new two-stage risk measures, we show that the risk measure of a partially observable system can be represented as a risk measure of a fully observable system that is defined by a g-evaluation. The innovation process of the original system is the Brownian Motion driving the new fully observable system. Furthermore, we introduce a risk-averse control problem for the partially observable system and we derive a risk-averse dynamic programming equation.
NotePh.D.
NoteIncludes bibliographical references
Noteby Ruofan Yan
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3D221VW
Languageeng
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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