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dc.contributor.authorAlbert, Carlo-
dc.contributor.authorUlzega, Simone-
dc.contributor.authorStoop, Ruedi-
dc.date.accessioned2019-01-29T18:36:49Z-
dc.date.available2019-01-29T18:36:49Z-
dc.date.issued2016-
dc.identifier.issn2470-0045de_CH
dc.identifier.issn2470-0053de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/14773-
dc.description.abstractParameter inference is a fundamental problem in data-driven modeling. Given observed data that is believed to be a realization of some parameterized model, the aim is to find parameter values that are able to explain the observed data. In many situations, the dominant sources of uncertainty must be included into the model, for making reliable predictions. This naturally leads to stochastic models. Stochastic models render parameter inference much harder, as the aim then is to find a distribution of likely parameter values. In Bayesian statistics, which is a consistent framework for data-driven learning, this so-called posterior distribution can be used to make probabilistic predictions. We propose a novel, exact and very efficient approach for generating posterior parameter distributions, for stochastic differential equation 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, where the measurements are mapped on heavier beads compared to those of the simulated data. To arrive at distribution samples, we employ a Hamiltonian Monte Carlo approach combined with a multiple time-scale integration. A separation of time scales naturally arises if either the number of measurement points or the number of simulation points becomes large. Furthermore, at least for 1D problems, we can decouple the harmonic modes between measurement points and solve the fastest part of their dynamics analytically. Our approach is applicable to a wide range of inference problems and is highly parallelizable.de_CH
dc.language.isoende_CH
dc.publisherAmerican Physical Societyde_CH
dc.relation.ispartofPhysical Review Ede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectComputer Science - Data Structures and Algorithmsde_CH
dc.subjectStatistics - Computationde_CH
dc.subject.ddc003: Systemede_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleBoosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separationde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.1103/PhysRevE.93.043313de_CH
dc.identifier.pmid27176434de_CH
zhaw.funding.euNode_CH
zhaw.issue4de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume93de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedBiomedical Simulationde_CH
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Albert, C., Ulzega, S., & Stoop, R. (2016). Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation. Physical Review E, 93(4). https://doi.org/10.1103/PhysRevE.93.043313
Albert, C., Ulzega, S. and Stoop, R. (2016) ‘Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation’, Physical Review E, 93(4). Available at: https://doi.org/10.1103/PhysRevE.93.043313.
C. Albert, S. Ulzega, and R. Stoop, “Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation,” Physical Review E, vol. 93, no. 4, 2016, doi: 10.1103/PhysRevE.93.043313.
ALBERT, Carlo, Simone ULZEGA und Ruedi STOOP, 2016. Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation. Physical Review E. 2016. Bd. 93, Nr. 4. DOI 10.1103/PhysRevE.93.043313
Albert, Carlo, Simone Ulzega, and Ruedi Stoop. 2016. “Boosting Bayesian Parameter Inference of Nonlinear Stochastic Differential Equation Models by Hamiltonian Scale Separation.” Physical Review E 93 (4). https://doi.org/10.1103/PhysRevE.93.043313.
Albert, Carlo, et al. “Boosting Bayesian Parameter Inference of Nonlinear Stochastic Differential Equation Models by Hamiltonian Scale Separation.” Physical Review E, vol. 93, no. 4, 2016, https://doi.org/10.1103/PhysRevE.93.043313.


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