State duration modelling in hidden Markov models
Signal Processing
Alternatives to Variable Duration HMM in Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Unsupervised signal restoration using hidden Markov chains with copulas
Signal Processing
Stylized facts of financial time series and hidden semi-Markov models
Computational Statistics & Data Analysis
Multisensor triplet Markov chains and theory of evidence
International Journal of Approximate Reasoning
Semi-Markov Chains and Hidden Semi-Markov Models toward Applications: Their Use in Reliability and DNA Analysis
Detection of multiple changes in fractional integrated ARMA processes
IEEE Transactions on Signal Processing
An equivalence of the EM and ICE algorithm for exponential family
IEEE Transactions on Signal Processing
Context-Sensitive Hidden Markov Models for Modeling Long-Range Dependencies in Symbol Sequences
IEEE Transactions on Signal Processing
Signal and image segmentation using pairwise Markov chains
IEEE Transactions on Signal Processing
Partially hidden Markov models
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Unsupervised segmentation of hidden semi-Markov non-stationary chains
Signal Processing
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The hidden Markov chain (HMC) model is a couple of random sequences (X,Y), in which X is an unobservable Markov chain, and Y is its observable ''noisy version''. The chain X is a Markov one and the components of Y are independent conditionally on X. Such a model can be extended in two directions: (i) X is a semi-Markov chain and (ii) the distribution of Y conditionally on X is a ''long dependence'' one. Until now these two extensions have been considered separately and the contribution of this paper is to consider them simultaneously. A new ''semi-Markov chain hidden with long dependence noise'' model is proposed and it is specified how it can be used to recover X from Y in an unsupervised manner. In addition, a new family of semi-Markov chains is proposed. Its advantages with respect to the classical formulations are the low computer time needed to perform different classical computations and the facility of its parameter estimation. Some experiments showing the interest of this new semi-Markov chain hidden with long dependence noise are also provided.