| Publikationstyp: | Konferenz: Sonstiges |
| Art der Begutachtung: | Keine Angabe |
| Titel: | Boosting Bayesian parameter inference of SDE models by Hamiltonian scale separation : a real-world case study in urban hydrology |
| Autor/-in: | Ulzega, Simone Albert, Carlo |
| et. al: | No |
| Angaben zur Konferenz: | 3rd biennial meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023 |
| Erscheinungsdatum: | 16-Mär-2023 |
| Sprache: | Englisch |
| Schlagwörter: | Bayesian data science; High performance computing; Hamiltonian Monte Carlo; Hydrology |
| Fachgebiet (DDC): | 510: Mathematik |
| Zusammenfassung: | In 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. |
| Weitere Angaben: | Invited talk, session "New tools for high-dimensional Bayesian inference from physics and ML" |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/27601 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Keine Angabe |
| 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 |
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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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