TY - JOUR

T1 - A MAP Estimate that Maximizes Entropy—An Alternative Interpretation for an Autoregressive Model

AU - Pillai, S. Unnikrishna

PY - 1985/4

Y1 - 1985/4

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84941463836&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84941463836&partnerID=8YFLogxK

U2 - 10.1109/PROC.1985.13208

DO - 10.1109/PROC.1985.13208

M3 - Article

AN - SCOPUS:84941463836

VL - 73

SP - 843

EP - 844

JO - Proceedings of the IEEE

JF - Proceedings of the IEEE

SN - 0018-9219

IS - 4

ER -