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dc.contributor.authorUlzega, Simone-
dc.description.abstractParameter inference is a fundamental problem in data-driven modeling. Indeed, for making reliable probabilistic predictions, model parameters need to be calibrated to measured data and their uncertainty needs to be quantified. Bayesian statistics is a consistent framework where knowledge about parameters is expressed through probability distributions. These so-called posterior distributions can become computationally very expensive to evaluate, especially with non-trivial stochastic models. We present a novel Hamiltonian Monte Carlo method for boosting Bayesian parameter inference of nonlinear stochastic differential equation models. The algorithm relies on the reinterpretation of the posterior distribution as the partition function of a statistical mechanics system akin to a polymer. We thus reduce the Bayesian inference problem to simulating the dynamics of a fictitious linear molecule whose dynamics are confined by the data and the model. Our approach is very efficient, applicable to a wide range of inference problems and highly parallelizable.de_CH
dc.rightsNot specifiedde_CH
dc.subject.ddc003: Systemede_CH
dc.titleA Hamiltonian Monte Carlo method for boosting Bayesian parameter inference of stochastic differential equation modelsde_CH
dc.typeKonferenz: Sonstigesde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.conference.detailsNDES 2017, 25th Nonlinear Dynamics of Electronic Systems Conference, Zernez, 5-7 June 2017de_CH
zhaw.publication.reviewNot specifiedde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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