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dc.contributor.authorUlzega, Simone-
dc.contributor.authorAlbert, Carlo-
dc.date.accessioned2023-04-15T12:37:34Z-
dc.date.available2023-04-15T12:37:34Z-
dc.date.issued2023-03-16-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/27601-
dc.descriptionInvited talk, session "New tools for high-dimensional Bayesian inference from physics and ML"de_CH
dc.description.abstractIn essentially all applied sciences, data-driven modeling heavily relies on a sound calibration of model parameters to measured data for making probabilistic predictions. Bayesian statistics is a consistent framework for parameter inference where knowledge about model parameters is expressed through probability distributions. However, Bayesian inference with stochastic models can become computationally extremely expensive and it is therefore hardly ever applied. We propose a very efficient approach for boosting Bayesian parameter inference of stochastic differential equation (SDE) models calibrated to measured time-series, using a Hamiltonian Monte Carlo (HMC) approach combined with a multiple time-scale integration. We present the first application of this HMC algorithm to a real-world case study from urban hydrology.de_CH
dc.language.isoende_CH
dc.rightsNot specifiedde_CH
dc.subjectBayesian data sciencede_CH
dc.subjectHigh performance computingde_CH
dc.subjectHamiltonian Monte Carlode_CH
dc.subjectHydrologyde_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleBoosting Bayesian parameter inference of SDE models by Hamiltonian scale separation : a real-world case study in urban hydrologyde_CH
dc.typeKonferenz: Sonstigesde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.conference.details3rd biennial meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewNot specifiedde_CH
zhaw.webfeedHigh Performance Computing (HPC)de_CH
zhaw.funding.zhawFeature Learning for Bayesian Inferencede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Ulzega, S., & Albert, C. (2023, March 16). Boosting Bayesian parameter inference of SDE models by Hamiltonian scale separation : a real-world case study in urban hydrology. 3rd Biennial Meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023.
Ulzega, S. and Albert, C. (2023) ‘Boosting Bayesian parameter inference of SDE models by Hamiltonian scale separation : a real-world case study in urban hydrology’, in 3rd biennial meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023.
S. Ulzega and C. Albert, “Boosting Bayesian parameter inference of SDE models by Hamiltonian scale separation : a real-world case study in urban hydrology,” in 3rd biennial meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023, Mar. 2023.
ULZEGA, Simone und Carlo ALBERT, 2023. Boosting Bayesian parameter inference of SDE models by Hamiltonian scale separation : a real-world case study in urban hydrology. In: 3rd biennial meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023. Conference presentation. 16 März 2023
Ulzega, Simone, and Carlo Albert. 2023. “Boosting Bayesian Parameter Inference of SDE Models by Hamiltonian Scale Separation : A Real-World Case Study in Urban Hydrology.” Conference presentation. In 3rd Biennial Meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023.
Ulzega, Simone, and Carlo Albert. “Boosting Bayesian Parameter Inference of SDE Models by Hamiltonian Scale Separation : A Real-World Case Study in Urban Hydrology.” 3rd Biennial Meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023, 2023.


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