Adaptive estimation of hidden nearly completely decomposable Markov chains

Author

Krishnamurthy, Vikram

Author_Institution

Dept. of Syst. Eng., Australian Nat. Univ., Canberra, ACT, Australia

Volume

iv

fYear

1994

fDate

19-22 Apr 1994

Abstract

We propose maximum-likelihood (ML) estimation schemes for nearly completely decomposable Markov chains (NCDMC) in white Gaussian noise. Aggregation techniques based on stochastic complementation are applied to reduce the dimension of the resulting hidden Markov model (HMM) and hence substantially reduce the computational costs of the estimation algorithms. We then present an aggregation based expectation maximization (EM) algorithm for estimating the parameters and states of the HMM

Keywords

FIR filters; Gaussian noise; adaptive equalisers; adaptive estimation; adaptive signal processing; hidden Markov models; maximum likelihood estimation; optimisation; state estimation; white noise; adaptive estimation; aggregation techniques; computational costs; estimation algorithms; expectation maximization algorithm; hidden Markov model; hidden nearly completely decomposable Markov chains; maximum-likelihood estimation schemes; stochastic complementation; white Gaussian noise; Adaptive estimation; Australia; Computational efficiency; Gaussian noise; Hidden Markov models; Maximum likelihood estimation; Parameter estimation; State estimation; Stochastic processes; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on

Conference_Location

Adelaide, SA

ISSN

1520-6149

Print_ISBN

0-7803-1775-0

Type

conf

DOI

10.1109/ICASSP.1994.389811

Filename

389811