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.
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U2 - 10.1109/PROC.1985.13208
DO - 10.1109/PROC.1985.13208
M3 - Article
AN - SCOPUS:84941463836
SN - 0018-9219
VL - 73
SP - 843
EP - 844
JO - Proceedings of the IEEE
JF - Proceedings of the IEEE
IS - 4
ER -