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dc.contributor.advisorDshalalow, Jewgeni H.
dc.contributor.authorWhite, Ryan Tyler
dc.date.accessioned2015-06-10T20:39:44Z
dc.date.available2015-06-10T20:39:44Z
dc.date.issued2015-05
dc.identifier.urihttp://hdl.handle.net/11141/680
dc.descriptionThesis (Ph.D.) - Florida Institute of Technology, 2015en_US
dc.description.abstractThis work studies a class of continuous-time, multidimensional random walk processes with mutually dependent random step sizes and their exits from hyperrectangles. Fluctuations of the process about the critical boundary are studied extensively by stochastic analysis and operational calculus. Further, information on the process can be ascertained only upon observations occurring according to a delayed renewal process, rather than in real time. Passage times are thus obscured and results are first derived pertaining to the pre-passage and post-passage observations. Two distinct strategies are developed to combat the crudeness of delayed observations in order to derive more refined information about the processes. The first strategy is to introduce intermediate thresholds on some of the coordinates and considers fluctuations about these intermediate boundaries, which can use information observed over time to continually refine the results. The second "time-sensitive" strategy restricts time to a random time interval, e.g. between the pre-passage and post-passage observations, and revives the real-time paths of the process from the delayed time series. This strategy leads to time-dependent probabilistic results, including joint distributions and conditional distributions and probabilities. In all models, probabilistic results (joint probability transforms under operators, marginal transforms, moments, distributions, probabilities) associated with passage times, excess levels, and the likelihood of threshold(s) to be crossed are derived, and shown to be analytically and numerically tractable under a variety of special cases. Results are tested for accuracy via stochastic simulation. The processes are applied to the detection and prediction of losses to vital networks due to hostile attacks and/or benign failures. The accumulation of losses to a network during a series of loss events is modeled by a 2-dimensional process. The first dimension counts the random numbers of nodes (e.g. routers or operational sites) incapacitated by successive attacks. The nodes have random weights associated with their incapacitation (e.g. loss of operational capacity or cost of repair). The second dimension measures the cumulative weight associated with the nodes lost. The exit from a rectangle corresponds to either type of loss surpassing a threshold, and represents the network entering a critical state.en_US
dc.language.isoen_USen_US
dc.rightsCopyright held by author.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectCommunication Networksen_US
dc.subjectExit Time
dc.subjectFirst Passage Time
dc.subjectFluctuation Theory
dc.subjectIndependent and Stationary Increment Processes
dc.subjectMarked Point Process
dc.subjectMarked Random Measure
dc.subjectMarked Random Walk
dc.subjectNetwork Models
dc.subjectOperational Calculus
dc.subjectPoisson Process
dc.subjectProbability
dc.subjectRandom Walk
dc.subjectRenewal Process
dc.subjectRenewal Theory
dc.subjectRuin Time
dc.subjectStochastic Networks
dc.subjectSums of Independent Random Variables
dc.subjectStochastic Analysis
dc.subjectStochastic System Simulation
dc.titleRandom Walks on Random Lattices and Their Applicationsen_US
dc.typeDissertationen_US
thesis.degree.nameDoctor of Philosophy in Applied Mathematicsen_US
thesis.degree.levelDoctoralen_US
thesis.degree.disciplineApplied Mathematicsen_US
thesis.degree.departmentMathematical Sciencesen_US
thesis.degree.grantorFlorida Institute of Technologyen_US


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