Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.745288
Title: Deterministic approximation schemes with computable errors for the distributions of Markov chains
Author: Kuntz Nussio, Juan
ISNI:       0000 0004 7223 4535
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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Abstract:
This thesis is a monograph on Markov chains and deterministic approximation schemes that enable the quantitative analysis thereof. We present schemes that yield approximations of the time-varying law of a Markov chain, of its stationary distributions, and of the exit distributions and occupation measures associated with its exit times. In practice, our schemes reduce to solving systems of linear ordinary differential equations, linear programs, and semidefinite pro- grams. We focus on the theoretical aspects of these schemes, proving convergence and providing computable error bounds for most of them. To a lesser extent, we study their practical use, applying them to a variety of examples and discussing the numerical issues that arise in their implementation.
Supervisor: Stan, Guy-Bart ; Barahona, Mauricio Sponsor: Biotechnology and Biological Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.745288  DOI:
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