Abstract
Let X(t) be a random function of time t, where t Є [a,b], − ∞ < a < b < ∞ or t Є [a; [),or t Є (−∞, ∞), as the specific case dictates. One way to describe the observable properties of the set of random functions, a.k.a. (also known as) a stochastic variable, is by means of a collection of correlation functions
for all \(l \geq 1\) If the functions in the set {C l } are pointwise defined, then X(l) is called a stochastic process. On the other hand, if the functions in the set are distributional in nature, then X(l is called a generalized stochastic process. We will have occasion to discuss both types.
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© 2011 Birkhäuser Boston
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Klauder, J.R. (2011). Stochastic Variable Theory. In: A Modern Approach to Functional Integration. Applied and Numerical Harmonic Analysis. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4791-9_4
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DOI: https://doi.org/10.1007/978-0-8176-4791-9_4
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