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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Bibliographic Details
Published in:	Stochastic environmental research and risk assessment Vol. 37; no. 8; pp. 3041 - 3061
Main Authors:	Bacci, Marco, Sukys, Jonas, Reichert, Peter, Ulzega, Simone, Albert, Carlo
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023 Springer Nature B.V
Subjects:	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
Online Access:	Get full text
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