A general framework for probabilistic sensitivity analysis with respect to distribution parameters

نویسندگان

چکیده

Probabilistic sensitivity analysis identifies the influential uncertain input to guide decision-making. We propose a general framework with respect distribution parameters that unifies wide range of measures, including information theoretical metrics such as Fisher information. The is derived analytically via constrained maximisation and reformulated into an eigenvalue problem. There are only two main steps implement utilising likelihood ratio/score function method, Monte Carlo type sampling followed by solving equation. resulting eigenvectors then provide directions for simultaneous variations focus perturb uncertainty most. Not it conceptually simple, but numerical examples demonstrate proposed also provides new insights, combined multiple correlated metrics, robust entropic constraint, approximation deterministic sensitivities. Three different examples, ranging from simple cantilever beam offshore marine riser, used potential applications applied mechanics problems.

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ژورنال

عنوان ژورنال: Probabilistic Engineering Mechanics

سال: 2023

ISSN: ['1878-4275', '0266-8920']

DOI: https://doi.org/10.1016/j.probengmech.2023.103433