Pairwise and Uniformly Hidden Markov Fields

2009 ◽  
Author(s):  
Wojciech Pieczynski ◽  
George Maroulis ◽  
Theodore E. Simos
Keyword(s):  
Hidden Markov ◽  
Markov Fields

scholarly journals Bloking Modification of Gibbs Sampling for Recognition of Hidden Markov Fields

2018 ◽  
pp. 31-41
Author(s):  
Yevgeniy V. Vodolazskiy ◽  
◽  
Serhiy A. Latiuk ◽  
Keyword(s):  
Gibbs Sampling ◽  
Hidden Markov ◽  
Markov Fields

Unsupervised segmentation of hidden Markov fields corrupted by correlated non-Gaussian noise

2018 ◽  
Vol 102 ◽  
pp. 41-59
Author(s):  
Lin An ◽  
Ming Li ◽  
Mohamed El Yazid Boudaren ◽  
Wojciech Pieczynski
Keyword(s):  
Gaussian Noise ◽  
Hidden Markov ◽  
Unsupervised Segmentation ◽  
Markov Fields ◽  
Non Gaussian

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.

scholarly journals Spatial risk mapping for rare disease with hidden Markov fields and variational EM

2013 ◽  
Vol 7 (2) ◽  
pp. 1192-1216 ◽  
Author(s):  
Florence Forbes ◽  
Myriam Charras-Garrido ◽  
Lamiae Azizi ◽  
Senan Doyle ◽  
David Abrial
Keyword(s):  
Rare Disease ◽  
Hidden Markov ◽  
Risk Mapping ◽  
Spatial Risk ◽  
Markov Fields ◽  
Variational Em
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