Elucidating appealing features of differentiable auto-correlation functions: A study on the modified exponential kernel

• Published in: Probabilistic engineering mechanics Vol. 69; p. 103269
• Main Authors: Faes, Matthias G.R., Broggi, Matteo, Spanos, Pol D., Beer, Michael
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.07.2022; Elsevier Science Ltd
• Subjects: Autocorrelation; Autocorrelation functions; Autoregressive processes; Convergence analysis; Correlation analysis; Kernels; Markov analysis; Markov processes; Partial differential equations; Random field; Representations; Series expansion; Stochastic analysis; Stochastic models; Stochastic processes; Random field; Stochastic analysis; Autocorrelation; Convergence analysis

Research on stochastic processes in recent decades has pointed out that, in the context of modeling spatial or temporal uncertainties, auto-correlation functions that are differentiable at the origin have advantages over functions that are not differentiable. For instance, the non-differentiability...