• DocumentCode
    2621764
  • Title

    On a relation between the principle of minimum relative entropy and maximum likelihood estimation

  • Author

    Tzannes, M.A. ; Noonan, J.P.

  • Author_Institution
    Dept. of Electr. Eng., Tufts Univ., Medford, MA, USA
  • fYear
    1990
  • fDate
    1-3 May 1990
  • Firstpage
    2132
  • Abstract
    A justification for the use of the minimum relative entropy (MRE) principle as a probability density function (PDF) estimation method is given by showing a relation with the classical maximum likelihood estimation procedure. This ultimately provides a noninformation theoretic argument for the validity of the MRE principle as well as some properties that the MRE PDF obeys
  • Keywords
    entropy; estimation theory; minimisation; probability; signal processing; maximum likelihood estimation; minimum relative entropy; probability density function; Arithmetic; Constraint theory; Digital signal processing; Entropy; Estimation theory; Maximum likelihood estimation; Minimization methods; Parameter estimation; Probability density function; Signal processing algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1990., IEEE International Symposium on
  • Conference_Location
    New Orleans, LA
  • Type

    conf

  • DOI
    10.1109/ISCAS.1990.112235
  • Filename
    112235
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2621764