|Term, with Synonyms and Cross-References||Definition||Notes and Comments|
See also probability.
|Pertaining to a sequence of observations, each of which can be considered to be a sample from a probability distribution.||Often informally used as a synonym of “probabilistic.”|
See also probability elicitation.
|Expert judgment about uncertain events or quantities, in the form of probability statements about future events. It is not based on any precise computation but is often a reasonable assessment by a knowledgeable person.|
See also probability, aleatoric probability, epistemic uncertainty.
|The condition of being unsure about something; a lack of assurance or conviction.c||For the purpose of this report, uncertainty is often described regarding a QOI of the true, physical system. This uncertainty depends on a model-based prediction, as well as on other information included in the VVUQ assessment. This uncertainty can be described using probability.|
|uncertainty quantification (UQ)||The process of quantifying uncertainties in a computed QOI, with the goals of accounting for all sources of uncertainty and quantifying the contributions of specific sources to the overall uncertainty.||More broadly, UQ can be thought of as the field of research that uses and develops theory, methodology, and approaches for carrying out inference, with the aid of computational models, on complex systems.|
|validation||The process of determining the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model.p|
See also standard deviation.
|The second moment of a probability distribution, defined as E(X - µ)2, where µ is the first moment of the random variable X.||The variance is a common measure of variability around the mean of a distribution. Its square root, the standard deviation, having dimensional units of the random variable, is a more intuitively meaningful measure of dispersion from the mean.|
See also code verification, solution verification.
|The process of determining whether a computer program (“code”) correctly solves the mathematical-model equations. This includes code verification (determining whether the code correctly implements the intended algorithms) and solution verification (determining the accuracy with which the algorithms solve the mathematical-model equations for specified QOIs).|
b Cornell LCS Statistics Laboratory. See http://instruct1.cit.cornell.edu:8000/courses/statslab/Stuff/indes.php.
c American Heritage Dictionary. 2000. Boston: Houghton, Mifflin.
d Glossary of Statistics Terms. Available at http://www.stat.berkeley.edu/users/stark/SticiGui/Text/gloss.htm.
f W. Feller. 1968. An Introduction to Probability Theory and Its Applications. New York, N.Y.: Wiley.
g J.L. Devore. 2000. Probability and Statistics for Engineering and the Sciences. Pacific Grove, Calif.: Duxbury Press.