Titlebook: Detection of Random Signals in Dependent Gaussian Noise; Antonio F. Gualtierotti Book 2015 Springer International Publishing Switzerland 2

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Overthrow

PLE

adduction

floodgate

Foreshadow

Scope of the Signal Plus “White Noise” Model (II)the Cramér-Hida framework, it becomes important to know that such a representation exists, as it is that representation which allows an explicit form for the likelihood. In the previous chapter, it was seen that the existence of the likelihood has the consequence that the observations are represente

Foreshadow

卜聞

Relations Between Reproducing Kernel Hilbert Spacesd RKHS’s, and the entire topic may be seen as a partial answer to the following question: given an RKHS of signals, which noises does it accommodate? What follows covers thus relations between RKHS’s and, in particular inclusions and intersections.

ALLAY

Cramér-Hida Representations from “First Principles”hich have a representation as integrals, with respect to an orthogonal vector measure, itself determined by a function with orthogonal increments. The decomposition is obtained under some “natural” assumptions [Assumption 6.4.1].

AVOID

Likelihoods for Signal Plus “White Noise” Versus “White Noise”ase, the road to the likelihood is based on a version of Girsanov’s theorem, and the likelihood itself follows when the “signal plus noise,” that is, the observation process, has a representation as the solution of a stochastic differential equation.