A comparison of numerical approaches for statistical inference with stochastic models

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 comm...

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Podrobná bibliografie
Vydáno v:	Stochastic environmental research and risk assessment Ročník 37; číslo 8; s. 3041 - 3061
Hlavní autoři:	Bacci, Marco, Sukys, Jonas, Reichert, Peter, Ulzega, Simone, Albert, Carlo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023 Springer Nature B.V
Témata:	Aquatic Pollution Chemistry and Earth Sciences Computational Intelligence Computer Science Earth and Environmental Science Earth Sciences Environment Hydrologic data Hydrologic models Hydrology Markov chains Math. Appl. in Environmental Science Mathematical models Original Paper Physics Probability Theory and Stochastic Processes Statistical analysis Statistical inference Statistics for Engineering Stochastic models Stochasticity Uncertainty Waste Water Technology Water Management Water Pollution Control Stochastic model Particle Markov Chain Monte Carlo Hamiltonian Monte Carlo Uncertainty quantification Calibration Bayesian inference Gibbs sampling
ISSN:	1436-3240, 1436-3259
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1436-3240 1436-3259
DOI:	10.1007/s00477-023-02434-z