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This problem leads to a multi-objective optimization with probabilistic constraints.
In this paper, two approaches to determine the satisfaction of probabilistic constraints are discussed.
Second, this method improves non-identifiability more reasonably, as probabilistic constraints not deterministic constraints (simplifying the model) are used.
Thus, the Bayesian models are non-identifiable from the perspective of probabilistic constraints [ 5 ].
Problems with multiple probabilistic constraints are also discussed.
This paper discusses geometric programs with joint probabilistic constraints.
In the SLDM-IDE, all of the probabilistic constraints are converted to the approximate deterministic constraints.
And the probabilistic constraints are divided into feasible, active and violated categories, and only violated and active probabilistic constraints are evaluated precisely.
Disturbances are modeled as Gaussian allowing for an explicit transformation of the probabilistic constraints into simple algebraic constraints.
We formulate these features as deterministic constraints and probabilistic constraints to prune false positives and effectively prioritize remaining faults.
Then the probabilistic constraints are transformed into deterministic forms by a single-loop deterministic method.
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