System Identification Under Minimum Error Entropy Criteria

In previous chapter, we give an overview of information theoretic parameter estimation. These estimation methods are, however, devoted to cases where a large amount of statistical information on the unknown parameter is assumed to be available. For example, the minimum divergence estimation needs to know the likelihood function of the parameter. Also, in Bayes estimation with minimum error entropy (MEE) criterion, the joint distribution of unknown parameter and observation is assumed to be known. In this and later chapters, we will further investigate information theoretic system identification. Our focus is mainly on system parameter estimation (identification) where no statistical ...

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