There are many areas of structural safety and structural dynamics in which it is often desirable
to compute the first few statistical moments of a function of random variables. The usual approximation is by
the Taylor expansion method. This approach requires the computation of derivatives. In order to avoid the
computation of derivatives, point estimates for probability moments have been proposed. However, the accuracy
is quite low, and sometimes, the estimating points may be outside the region in which the random variable is
defined. In the present paper, new point estimates for probability moments are proposed, in which increasing
the number of estimating points is easier because the estimating points are independent of the random variable
in its original space and the use of high-order moments of the random variables is not required. By using this
approximation, the practicability and accuracy of point estimates can be much improved.
There are many areas of structural safety and structural dynamics in which it is often desirable
to compute the first few statistical moments of a function of random variables. The usual approximation is by
the Taylor expansion method. This approach requires the computation of derivatives. In order to avoid the
computation of derivatives, point estimates for probability moments have been proposed. However, the accuracy
is quite low, and sometimes, the estimating points may be outside the region in which the random variable is
defined. In the present paper, new point estimates for probability moments are proposed, in which increasing
the number of estimating points is easier because the estimating points are independent of the random variable
in its original space and the use of high-order moments of the random variables is not required. By using this
approximation, the practicability and accuracy of point estimates can be much improved.
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