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dc.contributor.authorUlzega, Simone-
dc.date.accessioned2018-07-09T13:32:56Z-
dc.date.available2018-07-09T13:32:56Z-
dc.date.issued2017-04-11-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/7743-
dc.descriptionInvited seminar at ENS, Parisde_CH
dc.description.abstractParameter inference is a fundamental problem in data-driven modeling. The aim is to find a so-called posterior distribution of model parameters that are able to explain observed data and can be used for making probabilistic predictions. We propose a novel, exact, very efficient and highly parallelizable Hamiltonian Monte Carlo approach for generating posterior parameter distributions, for stochastic models calibrated to measured time-series. The algorithm is inspired by re-interpreting the posterior distribution as a statistical mechanics partition function of an object akin to a polymer, whose dynamics is confined by both the model and the measurements.de_CH
dc.language.isoende_CH
dc.rightsNot specifiedde_CH
dc.subject.ddc003: Systemede_CH
dc.titleBoosting parameter inference with stochastic models using molecular dynamics and high-performance computingde_CH
dc.typeVorlesungde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.originated.zhawYesde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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