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Cauchy–Schwarz inequality (Modified Schwarz inequality for 2-positive maps [27]) — For a 2-positive map between C*-algebras, for all , in its domain, () ‖ ‖ (), ‖ ‖ ‖ ‖ ‖ ‖. Another generalization is a refinement obtained by interpolating between both sides of the Cauchy–Schwarz inequality:
There are three inequalities between means to prove. There are various methods to prove the inequalities, including mathematical induction, the Cauchy–Schwarz inequality, Lagrange multipliers, and Jensen's inequality. For several proofs that GM ≤ AM, see Inequality of arithmetic and geometric means.
The Cauchy distribution is often used in statistics as the canonical example of a "pathological" distribution since both its expected value and its variance are undefined (but see § Moments below). The Cauchy distribution does not have finite moments of order greater than or equal to one; only fractional absolute moments exist. [1]
This inequality is valid no matter whether the Schwartz kernel (,) is non-negative or not. A similar statement about L p → L q {\displaystyle L^{p}\to L^{q}} operator norms is known as Young's inequality for integral operators : [ 3 ]
Cauchy-Schwarz inequality [ edit ] The Cauchy-Schwarz inequality for complex random variables, which can be derived using the Triangle inequality and Hölder's inequality , is
The Paley–Zygmund inequality is sometimes used instead of the Cauchy–Schwarz inequality and may occasionally give more refined results. Under the (incorrect) assumption that the events v , u in K are always independent, one has Pr ( v , u ∈ K ) = Pr ( v ∈ K ) Pr ( u ∈ K ) {\displaystyle \Pr(v,u\in K)=\Pr(v\in K)\,\Pr(u\in K)} , and ...
The Cauchy–Schwarz inequality shows that ... Assume that = is an estimator with expectation (based on the ...
Cauchy's inequality may refer to: the Cauchy–Schwarz inequality in a real or complex inner product space Cauchy's estimate , also called Cauchy's inequality, for the Taylor series coefficients of a complex analytic function