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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-27687
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dc.contributor.authorBacci, Marco-
dc.contributor.authorSukys, Jonas-
dc.contributor.authorReichert, Peter-
dc.contributor.authorUlzega, Simone-
dc.contributor.authorAlbert, Carlo-
dc.date.accessioned2023-04-21T08:43:22Z-
dc.date.available2023-04-21T08:43:22Z-
dc.date.issued2023-04-13-
dc.identifier.issn1436-3240de_CH
dc.identifier.issn1436-3259de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/27687-
dc.description.abstractDue to our limited knowledge about complex environmental systems, our predictions of their behavior under different scenarios or decision alternatives are subject to considerable uncertainty. As this uncertainty can often be relevant for societal decisions, the consideration, quantification and communication of it is very important. Due to internal stochasticity, often poorly known influence factors, and only partly known mechanisms, in many cases, a stochastic model is needed to get an adequate description of uncertainty. As this implies the need to infer constant parameters, as well as the time-course of stochastic model states, a very high-dimensional inference problem for model calibration has to be solved. This is very challenging from a methodological and a numerical perspective. To illustrate aspects of this problem and show options to successfully tackle it, we compare three numerical approaches: Hamiltonian Monte Carlo, Particle Markov Chain Monte Carlo, and Conditional Ornstein-Uhlenbeck Sampling. As a case study, we select the analysis of hydrological data with a stochastic hydrological model. We conclude that the performance of the investigated techniques is comparable for the analyzed system, and that also generality and practical considerations may be taken into account to guide the choice of which technique is more appropriate for a particular application.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofStochastic Environmental Research and Risk Assessmentde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectBayesian inferencede_CH
dc.subjectStochastic modelde_CH
dc.subjectHamiltonian Monte Carlode_CH
dc.subjectUncertainty quantificationde_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleA comparison of numerical approaches for statistical inference with stochastic modelsde_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.doi10.1007/s00477-023-02434-zde_CH
dc.identifier.doi10.21256/zhaw-27687-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf169295de_CH
zhaw.webfeedBiomedical Simulationde_CH
zhaw.webfeedHigh Performance Computing (HPC)de_CH
zhaw.funding.zhawFeature Learning for Bayesian Inferencede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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Bacci, M., Sukys, J., Reichert, P., Ulzega, S., & Albert, C. (2023). A comparison of numerical approaches for statistical inference with stochastic models. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-023-02434-z
Bacci, M. et al. (2023) ‘A comparison of numerical approaches for statistical inference with stochastic models’, Stochastic Environmental Research and Risk Assessment [Preprint]. Available at: https://doi.org/10.1007/s00477-023-02434-z.
M. Bacci, J. Sukys, P. Reichert, S. Ulzega, and C. Albert, “A comparison of numerical approaches for statistical inference with stochastic models,” Stochastic Environmental Research and Risk Assessment, Apr. 2023, doi: 10.1007/s00477-023-02434-z.
BACCI, Marco, Jonas SUKYS, Peter REICHERT, Simone ULZEGA und Carlo ALBERT, 2023. A comparison of numerical approaches for statistical inference with stochastic models. Stochastic Environmental Research and Risk Assessment. 13 April 2023. DOI 10.1007/s00477-023-02434-z
Bacci, Marco, Jonas Sukys, Peter Reichert, Simone Ulzega, and Carlo Albert. 2023. “A Comparison of Numerical Approaches for Statistical Inference with Stochastic Models.” Stochastic Environmental Research and Risk Assessment, April. https://doi.org/10.1007/s00477-023-02434-z.
Bacci, Marco, et al. “A Comparison of Numerical Approaches for Statistical Inference with Stochastic Models.” Stochastic Environmental Research and Risk Assessment, Apr. 2023, https://doi.org/10.1007/s00477-023-02434-z.


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