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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.
This characterization is used to specify intervals by mean of interval notation, which is described below. An open interval does not include any endpoint, and is indicated with parentheses. [2] For example, (,) = {< <} is the interval of all real numbers greater than 0 and less than 1.
Jensen's inequality can be proved in several ways, and three different proofs corresponding to the different statements above will be offered. Before embarking on these mathematical derivations, however, it is worth analyzing an intuitive graphical argument based on the probabilistic case where X is a real number (see figure).
This calculator program has accepted input in infix notation, and returned the answer , ¯. Here the comma is a decimal separator. Here the comma is a decimal separator. Infix notation is a method similar to immediate execution with AESH and/or AESP, but unary operations are input into the calculator in the same order as they are written on paper.
In mathematical analysis, limit superior and limit inferior are important tools for studying sequences of real numbers.Since the supremum and infimum of an unbounded set of real numbers may not exist (the reals are not a complete lattice), it is convenient to consider sequences in the affinely extended real number system: we add the positive and negative infinities to the real line to give the ...
In mathematics, Chebyshev's sum inequality, named after Pafnuty Chebyshev, states that if ...
Muirhead's inequality states that [a] ≤ [b] for all x such that x i > 0 for every i ∈ { 1, ..., n} if and only if there is some doubly stochastic matrix P for which a = Pb. Furthermore, in that case we have [ a ] = [ b ] if and only if a = b or all x i are equal.
The bounds these inequalities give on a finite sample are less tight than those the Chebyshev inequality gives for a distribution. To illustrate this let the sample size N = 100 and let k = 3. Chebyshev's inequality states that at most approximately 11.11% of the distribution will lie at least three standard deviations away from the mean.