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The upper and lower bounds on the elastic modulus of a composite material, as predicted by the rule of mixtures. The actual elastic modulus lies between the curves. In materials science, a general rule of mixtures is a weighted mean used to predict various properties of a composite material.
The main objective of interval arithmetic is to provide a simple way of calculating upper and lower bounds of a function's range in one or more variables. These endpoints are not necessarily the true supremum or infimum of a range since the precise calculation of those values can be difficult or impossible; the bounds only need to contain the function's range as a subset.
The set S = {42} has 42 as both an upper bound and a lower bound; all other numbers are either an upper bound or a lower bound for that S. Every subset of the natural numbers has a lower bound since the natural numbers have a least element (0 or 1, depending on convention). An infinite subset of the natural numbers cannot be bounded from above.
There is a corresponding greatest-lower-bound property; an ordered set possesses the greatest-lower-bound property if and only if it also possesses the least-upper-bound property; the least-upper-bound of the set of lower bounds of a set is the greatest-lower-bound, and the greatest-lower-bound of the set of upper bounds of a set is the least ...
Thus, the infimum or meet of a collection of subsets is the greatest lower bound while the supremum or join is the least upper bound. In this context, the inner limit, lim inf X n, is the largest meeting of tails of the sequence, and the outer limit, lim sup X n, is the smallest joining of tails of the sequence. The following makes this precise.
This results in a sequence of intervals given by +:= [,] and = [,], where >, will provide accurate upper and lower bounds for very fast. In practice, only c n {\displaystyle c_{n}} has to be considered, which converges to x {\displaystyle {\sqrt {x}}} (as does of course the lower interval bound).
Stirling's formula is in fact the first approximation to the following series (now called the Stirling series): [6]! (+ + +). An explicit formula for the coefficients in this series was given by G. Nemes. [ 7 ]
Therefore, the lower limit of the prediction interval is approximately 5 ‒ (2⋅1) = 3, and the upper limit is approximately 5 + (2⋅1) = 7, thus giving a prediction interval of approximately 3 to 7. Diagram showing the cumulative distribution function for the normal distribution with mean (μ) 0 and variance (σ 2) 1.