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dc.contributor.authorVilanova, Pedro
dc.date.accessioned2017-06-08T06:32:30Z
dc.date.available2017-06-08T06:32:30Z
dc.date.issued2016-01-07
dc.identifier.urihttp://hdl.handle.net/10754/624859
dc.description.abstractIn this work, we present an extension of the forward-reverse representation introduced in Simulation of forward-reverse stochastic representations for conditional diffusions , a 2014 paper by Bayer and Schoenmakers to the context of stochastic reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, i.e., SRNs conditional on their values in the extremes of given time-intervals. We then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve a set of deterministic optimization problems where the SRNs are replaced by their reaction-rate ordinary differential equations approximation; then, during phase II, we apply the Monte Carlo version of the Expectation-Maximization algorithm to the phase I output. By selecting a set of over-dispersed seeds as initial points in phase I, the output of parallel runs from our two-phase method is a cluster of approximate maximum likelihood estimates. Our results are supported by numerical examples.
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/7b3c5a8ef3dd4724bb8a29e8d09642d11d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
dc.titleAn efficient forward-reverse expectation-maximization algorithm for statistical inference in stochastic reaction networks
dc.typePresentation
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.personVilanova, Pedro
refterms.dateFOA2018-06-13T14:46:48Z


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