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Titlebook: Maximum-Likelihood Deconvolution; A Journey into Model Jerry M. Mendel Book 1990 Springer-Verlag New York Inc. 1990 Signal.entropy.filter.f

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Properties and Performance,ar signal processing; and, ., which is also a form of nonlinear signal processing. In this chapter we describe everything that is known to-date about the properties and performance of these three types of signal processing. We do this in order to gain a better appreciation and understanding of these
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Examples,terial in Chapters 4 and 5. Real and synthetic data cases are presented, because much can be learned about MLD from both types of data. Rather than collect all of the real data examples in one section, at the end of the chapter, as is customarily done in journal articles, we shall weave them in with
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Maximizing Likelihood,lihood function, we shall focus on maximizing . {}. Maximum-likelihood values of the parameter vectors are denoted with a superscript ML, e.g., ., ... There are many different methods one can use to maximize .{}. We shall examine some of these in this chapter. First, however, we must be convinced that maximizing .{} is a meaningful thing to do.
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1431-7893 raveling of convolution. It is the inverse problem of generating the system‘s input from knowledge about the system‘s output and dynamics. Deconvolution requires a careful balancing of bandwidth and signal-to-noise ratio effects. Maximum-likelihood deconvolution (MLD) is a design procedure that hand
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Book 1990f convolution. It is the inverse problem of generating the system‘s input from knowledge about the system‘s output and dynamics. Deconvolution requires a careful balancing of bandwidth and signal-to-noise ratio effects. Maximum-likelihood deconvolution (MLD) is a design procedure that handles both e
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George E. Yoosoper should know. The book is simple and concise, giving readers an immediate return on their investment. After learning the lessons of this book, business process analysts and developers will be prepared to us978-1-4302-2751-9978-1-4302-2752-6
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