Boosting Bayesian parameter inference of SDE models by Hamiltonian scale separation : a real-world case study in urban hydrology

Ulzega, Simone; Albert, Carlo

Publication type:	Conference other
Type of review:	Not specified
Title:	Boosting Bayesian parameter inference of SDE models by Hamiltonian scale separation : a real-world case study in urban hydrology
Authors:	Ulzega, Simone Albert, Carlo
et. al:	No
Conference details:	3rd biennial meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023
Issue Date:	16-Mar-2023
Language:	English
Subjects:	Bayesian data science; High performance computing; Hamiltonian Monte Carlo; Hydrology
Subject (DDC):	510: Mathematics
Abstract:	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.
Further description:	Invited talk, session "New tools for high-dimensional Bayesian inference from physics and ML"
URI:	https://digitalcollection.zhaw.ch/handle/11475/27601
Fulltext version:	Published version
License (according to publishing contract):	Not specified
Departement:	Life Sciences and Facility Management
Organisational Unit:	Institute of Computational Life Sciences (ICLS)
Published as part of the ZHAW project:	Feature Learning for Bayesian Inference
Appears in collections:	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.