Unsupervised segmentation of triplet Markov chains hidden with long-memory noise

DOI10.1016/j.sigpro.2007.10.015zbMath1186.94189OpenAlexW2130227330MaRDI QIDQ971362

Wojciech Pieczynski, Jérôme Lapuyade-Lahorgue, Pierre Lanchantin

Publication date: 19 May 2010

Published in: Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.sigpro.2007.10.015

zbMATH Keywords

hidden Markov chainsiterative conditional estimationtriplet Markov chainslong-memory noisepairwise Markov chainsunsupervised Bayesian segmentation

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (10)

Fast algorithm based on triplet Markov fields for unsupervised multi-class segmentation of SAR images ⋮ Unsupervised segmentation of randomly switching data hidden with non-Gaussian correlated noise ⋮ Efficient Bayesian estimation of the multivariate double chain Markov model ⋮ On convergence of the iterative conditional estimations ⋮ Deep parameterizations of pairwise and triplet Markov models for unsupervised classification of sequential data ⋮ Exact Bayesian prediction in a class of Markov-switching models ⋮ Hidden semi-Markov models ⋮ Estimation of Viterbi path in Bayesian hidden Markov models ⋮ Unsupervised segmentation of new semi-Markov chains hidden with long dependence noise ⋮ Unsupervised data classification using pairwise Markov chains with automatic copulas selection

This page was built for publication: Unsupervised segmentation of triplet Markov chains hidden with long-memory noise