By C. R. Weisbin (auth.), Jeffery Lewins, Martin Becker (eds.)

ISBN-10: 1461334616

ISBN-13: 9781461334613

ISBN-10: 1461334632

ISBN-13: 9781461334637

**Read Online or Download Advances in Nuclear Science and Technology: Volume 14 Sensitivity and Uncertainty Analysis of Reactor Performance Parameters PDF**

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**Additional resources for Advances in Nuclear Science and Technology: Volume 14 Sensitivity and Uncertainty Analysis of Reactor Performance Parameters**

**Example text**

When two or more parameters, y, z, ... must be considered, one defines a joint density function, f(y,z, ••• ). ; in the nuclear case, many dimensions are required. The marginal f(y) of Equation (1) is obtained by integrating the joint density function over all the other variables. b. Variance, Covariance and Correlation. s. or standard uncertainty or deviation of y. We will write s(y) = [v(y))1/2 using the symbol s rather than the more standard a to avoid confusion with cross-sections. If the density function is "normal", there is a 68% probability that the loyl < s(y).

C. Least-Squares Inclusion of New Data. If a leastsquares solution with its output covariance properly combines all the data at its input, then one should be able to 44 R. W. PEELLE combine a new set of data with the solution to the previous problem rather than re-analyze the accumulation of data including the new set with all of the old. Assuming the parameters of the original evaluation (solution) are linear combinations of the parameters chosen to represent the evaluation of the combined data, one can readily show that the final result is formally indifferent to the process selected.

Note that theenumerical values of the observations y. are precisely identified as the expected values

### Advances in Nuclear Science and Technology: Volume 14 Sensitivity and Uncertainty Analysis of Reactor Performance Parameters by C. R. Weisbin (auth.), Jeffery Lewins, Martin Becker (eds.)

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