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dc.contributor.authorBayer, Christian
dc.contributor.authorMoraes, Alvaro
dc.contributor.authorTempone, Raul
dc.contributor.authorVilanova, Pedro
dc.date.accessioned2017-06-05T08:35:47Z
dc.date.available2017-06-05T08:35:47Z
dc.date.issued2015-01-07
dc.identifier.urihttp://hdl.handle.net/10754/624074
dc.description.abstractIn this work, we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem of approximating the reaction coefficients based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which we solve a set of deterministic optimization problems where the SRNs are replaced by the classical ODE rates; then, during the second phase, the Monte Carlo version of the EM algorithm is applied starting from the output of the previous phase. Starting from a set of over-dispersed seeds, the output of our two-phase method is a cluster of maximum likelihood estimates obtained by using convergence assessment techniques from the theory of Markov chain Monte Carlo.
dc.titleThe Forward-Reverse Algorithm for Stochastic Reaction Networks
dc.typePoster
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering (CEMSE)
dc.conference.dateJanuary 6-9, 2015
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)
dc.conference.locationKAUST
dc.contributor.institutionWeierstrass Institute for applied analysis and stochastics
kaust.personMoraes, Alvaro
kaust.personTempone, Raul
kaust.personVilanova, Pedro
refterms.dateFOA2018-06-14T03:19:29Z


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