A comparison of numerical approaches for statistical inference with stochastic models

• 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

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...