A MAP Estimate that Maximizes Entropy—An Alternative Interpretation for an Autoregressive Model

Research output: Contribution to journalArticle

Abstract

It is shown here that when extrapolation of a sequence of data with unknown statistics is performed under two optimization constraints, viz. maximizing the entropy and maximizing the a posteriori (MAP) probability density function (PDF) of the unknown sample, the resulting estimate is the same as that of an Autoregressive (AR) model. This leads to the conclusion that the estimate from an AR model is optimum in the sense that it is the MAP estimate which maximizes entropy.

Original languageEnglish (US)
Pages (from-to)843-844
Number of pages2
JournalProceedings of the IEEE
Volume73
Issue number4
DOIs
StatePublished - Apr 1985

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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