TY - GEN

T1 - Entropy and information rates for hidden Markov models

AU - Ko, Hanseok

AU - Baran, R. H.

PY - 1998

Y1 - 1998

N2 - A practical approach to statistical inference for hidden Markov models (HMMs) requires expressions for the mean and variance of the log-probability of an observed T-long sequence given the model parameters. From the viewpoint of Shannon theory, in the limit of large T, the expected value of the per step log-probability is minus one times the mean entropy rate at the output of a noisy channel driven by the Markov source. A novel procedure for finding the entropy rate is presented. The rate distortion function of the Markov source, subject to the requirement of instantaneous coding, is a by-product of the derivation.

AB - A practical approach to statistical inference for hidden Markov models (HMMs) requires expressions for the mean and variance of the log-probability of an observed T-long sequence given the model parameters. From the viewpoint of Shannon theory, in the limit of large T, the expected value of the per step log-probability is minus one times the mean entropy rate at the output of a noisy channel driven by the Markov source. A novel procedure for finding the entropy rate is presented. The rate distortion function of the Markov source, subject to the requirement of instantaneous coding, is a by-product of the derivation.

UR - http://www.scopus.com/inward/record.url?scp=84890370594&partnerID=8YFLogxK

U2 - 10.1109/ISIT.1998.708979

DO - 10.1109/ISIT.1998.708979

M3 - Conference contribution

AN - SCOPUS:84890370594

SN - 0780350006

SN - 9780780350007

T3 - IEEE International Symposium on Information Theory - Proceedings

SP - 374

BT - Proceedings - 1998 IEEE International Symposium on Information Theory, ISIT 1998

T2 - 1998 IEEE International Symposium on Information Theory, ISIT 1998

Y2 - 16 August 1998 through 21 August 1998

ER -