Nonparametric Maximum Entropy
AuthorPolitis, Dimitris Nicolas
SourceIEEE Transactions on Information Theory
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The standard maximum entropy method of Burg and the resulting autoregressive model has been widely applied for spectrum estimation and prediction. A generalization of the maximum entropy formalism in a nonparametric setting is presented, and the class of the resulting solutions is identified to be a class of Markov processes. The proof is based on a string of information theoretic arguments developed in Choi and Cover's derivation of Burg's maximum entropy spectrum. A framework for the practical implementation of the proposed method is also presented, in the context of both continuous and discrete data. © 1993, IEEE. All rights reserved.