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https://doi.org/10.21256/zhaw-27687| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | A comparison of numerical approaches for statistical inference with stochastic models |
| Autor/-in: | Bacci, Marco Sukys, Jonas Reichert, Peter Ulzega, Simone Albert, Carlo |
| et. al: | No |
| DOI: | 10.1007/s00477-023-02434-z 10.21256/zhaw-27687 |
| Erschienen in: | Stochastic Environmental Research and Risk Assessment |
| Erscheinungsdatum: | 13-Apr-2023 |
| Verlag / Hrsg. Institution: | Springer |
| ISSN: | 1436-3240 1436-3259 |
| Sprache: | Englisch |
| Schlagwörter: | Bayesian inference; Stochastic model; Hamiltonian Monte Carlo; Uncertainty quantification |
| Fachgebiet (DDC): | 510: Mathematik |
| Zusammenfassung: | Due 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/27687 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | Life Sciences und Facility Management |
| Organisationseinheit: | Institut für Computational Life Sciences (ICLS) |
| Publiziert im Rahmen des ZHAW-Projekts: | Feature Learning for Bayesian Inference |
| Enthalten in den Sammlungen: | Publikationen Life Sciences und Facility Management |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2023_Bacci-etal_A-comparison-of-numerical-approaches-for-statistical-inference-with-stochastic-models.pdf | 4.72 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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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