Turbo decoding of hidden Markov sources with unknown parameters

2002 ◽  
Author(s):  
J. Garcia-Frias ◽  
J.D. Villasenor
Keyword(s):  
Hidden Markov ◽  
Turbo Decoding ◽  
Unknown Parameters ◽  
Markov Sources

Joint turbo decoding and estimation of hidden Markov sources

2001 ◽  
Vol 19 (9) ◽  
pp. 1671-1679 ◽  
Author(s):  
J. Garcia-Frias ◽  
J.D. Villasenor
Keyword(s):  
Hidden Markov ◽  
Turbo Decoding ◽  
Markov Sources

scholarly journals Switched scalar quantizers for hidden Markov sources

1992 ◽  
Vol 38 (5) ◽  
pp. 1455-1473 ◽  
Author(s):  
D.M. Goblirsch ◽  
N. Farvardin
Keyword(s):  
Hidden Markov ◽  
Scalar Quantizers ◽  
Markov Sources

Remaining Useful Life Estimation Based on a Segmental Hidden Markov Model With Continuous Observations

2017 ◽  
Author(s):  
Zhen Chen ◽  
Tangbin Xia ◽  
Ershun Pan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Goodness Of Fit ◽  
Hidden Markov ◽  
Remaining Useful Life ◽  
Likelihood Method ◽  
Distribution Model ◽  
Unknown Parameters ◽  
Useful Life ◽  
Hidden States

In this paper, a segmental hidden Markov model (SHMM) with continuous observations, is developed to tackle the problem of remaining useful life (RUL) estimation. The proposed approach has the advantage of predicting the RUL and detecting the degradation states simultaneously. As the observation space is discretized into N segments corresponding to N hidden states, the explicit relationship between actual degradation paths and the hidden states can be depicted. The continuous observations are fitted by Gaussian, Gamma and Lognormal distribution, respectively. To select a more suitable distribution, model validation metrics are employed for evaluating the goodness-of-fit of the available models to the observed data. The unknown parameters of the SHMM can be estimated by the maximum likelihood method with the complete data. Then a recursive method is used for RUL estimation. Finally, an illustrate case is analyzed to demonstrate the accuracy and efficiency of the proposed method. The result also suggests that SHMM with observation probability distribution which is closer to the real data behavior may be more suitable for the prediction of RUL.

scholarly journals Characterization of Ergodic Hidden Markov Sources

2009 ◽  
Vol 55 (5) ◽  
pp. 2107-2118 ◽  
Author(s):  
Alexander Schonhuth ◽  
Herbert Jaeger
Keyword(s):  
Hidden Markov ◽  
Markov Sources

scholarly journals MCMC joint separation for the hidden Markov fields with particles

2020 ◽  
Author(s):  
Kevin Williams ◽  
Warren Washer ◽  
Brian Rees ◽  
Agustin Lott
Keyword(s):  
Data Augmentation ◽  
Hidden Markov ◽  
Hidden Variables ◽  
Real Data ◽  
Stationary Regime ◽  
Blind Separation ◽  
Unknown Parameters ◽  
Markov Fields ◽  
Mixing Matrix ◽  
Joint Separation

In this contribution, we consider the problem of the blind separation of noisy instantaneously mixed images. The images are modelized by hidden Markov fields with unknown parameters. Given the observed images, we give a Bayesian formulation and we propose to solve the resulting data augmentation problem by implementing a Monte Carlo Markov Chaîn (MCMC) procedure. We separate the unknown variables into two categories: \\$1$. The parameters of interest which are the mixing matrix, the noise covariance and the parameters of the sources distributions.\\$2$. The hidden variables which are the unobserved sources and the unobserved pixels classification labels.The proposed algorithm provides in the stationary regime samples drawn from the posterior distributions of all the variables involved in the problem leading to a flexibility in the cost function choice.We discuss and characterize some problems of non identifiability and degeneracies of the parameters likelihood and the behavior of the MCMC algorithm in this case. Finally, we show the results for both synthetic and real data to illustrate the feasibility of the proposed solution.

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