Adaptive estimation of hidden nearly completely decomposable Markov chains with applications in blind equalization

International Journal of Adaptive Control and Signal Processing

Volumn 8, Issue 3, 1994, Pages 237-260

  Krishnamurthy, Vikram ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

EM algorithm; Hidden Markov model; Markov chains; Nearly completely decomposable (NCD)

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; MATHEMATICAL MODELS; ON-OFF CONTROL SYSTEMS; PARAMETER ESTIMATION; PHASE SHIFT KEYING; RANDOM PROCESSES; STATE ESTIMATION; WHITE NOISE;

BLIND EQUALIZATION; EXPECTATION MAXIMIZATION ALGORITHM; HIDDEN NEARLY COMPLETELY DECOMPOSABLE MARKOV CHAINS; WHITE GAUSSIAN NOISE;

ADAPTIVE SYSTEMS;

EID: 0028427136     PISSN: 08906327     EISSN: 10991115     Source Type: Journal    
DOI: 10.1002/acs.4480080303     Document Type: Article

References (14)

1. Aggregation of variables in dynamic systems
2. A singular perturbation approach to modelling and control of Markov chains
3. Aggregation of the policy iteration method for nearly completely decomposable Markov chains
4. Stochastic complementation, uncoupling Markov chains, and the theory of nearly reducible systems
5. An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition
6. Maximum likelihood estimation from incomplete data via the EM algorithm
7. Sequential algorithms for parameter estimation based on the Kullback‐Leibler information measure
8. On‐line estimation of hidden Markov parameters based on the Kullback‐Leibler information measure
9. A new method for evaluating the log‐likelihood gradient (score) of linear dynamic systems
10. Acceleration of stochastic approximation by averaging