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dc.contributor.authorMoraes, Alvaro
dc.contributor.authorTempone, Raul
dc.contributor.authorVilanova, Pedro
dc.date.accessioned2017-06-08T06:32:26Z
dc.date.available2017-06-08T06:32:26Z
dc.date.issued2016-01-06
dc.identifier.urihttp://hdl.handle.net/10754/624784
dc.description.abstractMarkovian pure jump processes can model many phenomena, e.g. chemical reactions at molecular level, protein transcription and translation, spread of epidemics diseases in small populations and in wireless communication networks among many others. In this work we present a novel hybrid algorithm for simulating individual trajectories which adaptively switches between the SSA and the Chernoff tauleap methods. This allows us to: (a) control the global exit probability of any simulated trajectory, (b) obtain accurate and computable estimates for the expected value of any smooth observable of the process with minimal computational work.
dc.subjectSampling
dc.titleHybrid Chernoff Tau-Leap
dc.typePoster
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.dateJanuary 5-10, 2016
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)
dc.conference.locationKAUST
kaust.personMoraes, Alvaro
kaust.personTempone, Raul
kaust.personVilanova, Pedro
refterms.dateFOA2018-06-13T15:16:55Z


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