Title :
A MAP estimate that maximizes entropy—An alternative interpretation for an autoregressive model
Author :
Pillai, S. Unnikrishna
Author_Institution :
University of Pennsylvania, Philadelphia, PA, USA
fDate :
4/1/1985 12:00:00 AM
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.
Keywords :
Covariance matrix; Density functional theory; Entropy; Equations; Extrapolation; Gaussian distribution; Probability density function; Statistics; Systems engineering and theory; Writing;
Journal_Title :
Proceedings of the IEEE
DOI :
10.1109/PROC.1985.13208
