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To obtain the hyper-parameters of the priori distribution, the bootstrap method [32], an important estimate method through statistical variance for interval estimation, is applied in this paper.
Then the verification experiments are conduced to validate the estimate method.
The uncertainties have been incorporated in the formulation using point estimate method (PEM).
Our design approach is based on a combination of a point estimate method and a deterministic numerical analysis code.
The two-point estimate method (TPEM) is used to model the uncertainties associated with wind farms power generation.
Point estimate method is used to estimate the first four statistical moments of bearing strength of composite pinned joint.
The contribution of this paper consists of presenting the point estimate method for correlated random variables in the field of model-based robust process design.
The point estimate method requires only a few deterministic evaluations of the analyzed process model and estimates the statistical moments for robust optimization.
It is shown that all methods are stable and effective for this investigation; however, the Rosenblueth's 2K + 1 Point Estimate method requires the least computational cost.
In order to assess a hazard, which is expressed as a temporal probability, FOSM technique was used along with Rosenblueth's point estimate method.
The point estimate method is adopted to understand the uncertainty propagation and to estimate the probability of ULS failure and the probability of SLS failure.
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