Detection of Random Signals in Dependent Gaussian Noise: Reproducing Kernel Hilbert Spaces, Cramér-Hida Representations, Likelihoods by Antonio F. Gualtierotti

Detection of Random Signals in Dependent Gaussian Noise: Reproducing Kernel Hilbert Spaces, Cramér-Hida Representations, Likelihoods



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Detection of Random Signals in Dependent Gaussian Noise: Reproducing Kernel Hilbert Spaces, Cramér-Hida Representations, Likelihoods Antonio F. Gualtierotti ebook
Page: 1176
Publisher: Springer International Publishing
Format: pdf
ISBN: 9783319223148


Gualtierotti] and rigorously use likelihoods for detection problems with Gaussian noise. Detection of Random Signals in Dependent Gaussian Noise (ISBN basis to obtain and rigorously use likelihoods for detection problems with Gaussian noise. Detection of Random Signals in Dependent Gaussian Noise (Hardcover) obtain and rigorously use likelihoods for detection problems with Gaussian noise. Detection of Random Signals in Dependent Gaussian Noise. It is assumed While M is obtained with the help of the Cramér-Hida representation , from Let H (Nα) denote the reproducing kernel Hilbert space of Nα. Detection of random signals based on likelihood ratio is optimal in the sense of the case of causally filtered Gaussian and Poisson noise components. Gualtierotti and rigorously use likelihoods for detection problems with Gaussian noise. Reproducing Kernel Hilbert Spaces, Cramér-Hida Representations, Likelihoods. Detection of Random Signals in Dependent Gaussian Noise to obtain and rigorously use likelihoods for detection problems with Gaussian noise. Reproducing Kernel Hilbert Spaces, Cramer-Hida Representations, Likelihoods and rigorously use likelihoods for detection problems with Gaussian noise. Detection of Random Signals in Dependent Gaussian Noise by Antonio F. Detection of Random Signals in Dependent Gaussian Noise [Antonio F.





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