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Within this framework, the standard perturbation-based stochastic finite element method is relaxed in order to incorporate uncertain probabilities in static, dynamic and modal analyses.
In relation to the treatment of uncertain probabilities of failure, a new measure of 'probabilistic confidence' is proposed to account for the concern that something may be unsafe (in relation to a target level of safety) and may subsequently fail.
For uncertain probabilities of failure, a measure of 'probabilistic confidence' has been proposed to reflect the concern that uncertainty about the true probability of failure could result in a system or structure that is unsafe and could subsequently fail.
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Contingencies of uncertain probability tend to be of little interest to politicians, who are confident they know the future.
Robustness to available probability data is incorporated by minimizing the unreliability of fault detection with uncertain probability data.
Robustness to available probability data can be incorporated by maximizing reliability of the faults involving uncertain probability data.
This measure of 'probabilistic confidence' provides a rational basis for the determination of an appropriate characteristic value for an uncertain probability of failure.
In this paper, the bounded information on statistical moments is used to construct a family of Maximum Entropy distributions, leading to an uncertain probability function.
From information theory, the most uncertain probability distribution is the one that maximizes the entropy term: E = - mathop sum limits_{k} pleft( {A_{k} } right)ln p(A_{k} ) (5).
As always the answer comes in some pretty uncertain probability stats.
In the PG game, particularly in condition III (greed-free environment), the participants might fear losing money due to the uncertain probability of earning a bonus.
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