A comparison of numerical approaches for statistical inference with stochastic models

Bacci, Marco; Sukys, Jonas; Reichert, Peter; Ulzega, Simone; Albert, Carlo

doi:10.1007/s00477-023-02434-z

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-27687

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	A comparison of numerical approaches for statistical inference with stochastic models
Authors:	Bacci, Marco Sukys, Jonas Reichert, Peter Ulzega, Simone Albert, Carlo
et. al:	No
DOI:	10.1007/s00477-023-02434-z 10.21256/zhaw-27687
Published in:	Stochastic Environmental Research and Risk Assessment
Issue Date:	13-Apr-2023
Publisher / Ed. Institution:	Springer
ISSN:	1436-3240 1436-3259
Language:	English
Subjects:	Bayesian inference; Stochastic model; Hamiltonian Monte Carlo; Uncertainty quantification
Subject (DDC):	510: Mathematics
Abstract:	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
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
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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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.