Maximizing Likelihood

  • Jerry M. Mendel
Part of the Signal Processing and Digital Filtering book series (SIGNAL PROCESS)


We have shown that the deconvolution problem can be viewed as an optimization problem. The maximum-likelihood (ML) values of a, b, s, q, r, and uB are the values where either the likelihood function L{a, b, s, q, r, uBz} or the loglikelihood function L{a, b, s, q, r, uBz} attains its maximum. Because of the exponential nature of our likelihood function, we shall focus on maximizing L {}. Maximum-likelihood values of the parameter vectors are denoted with a superscript ML, e.g., aML, uBML. There are many different methods one can use to maximize L{}. We shall examine some of these in this chapter. First, however, we must be convinced that maximizing L{} is a meaningful thing to do.


Test Sequence Decision Function Decision Strategy Test Vector Separation Principle 
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Copyright information

© Springer-Verlag New York Inc. 1990

Authors and Affiliations

  • Jerry M. Mendel
    • 1
  1. 1.Department of Electrical Engineering-SystemsUniversity of Southern CaliforniaLos AngelesUSA

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