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Hidden hybrid Markov/semi-Markov chains [PDF]
Computational Statistics & Data Analysis, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Y. Guédon
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Unsupervised segmentation of hidden semi-Markov non-stationary chains [PDF]
Signal Processing, 2006 In the classical hidden Markov chain (HMC) model we have a hidden chain X, which is a Markov one and an observed chain Y. HMC are widely used; however, in some situations they have to be replaced by the more general “hidden semi‐Markov chains” (HSMC) which are particular “triplet Markov chains” (TMC) T = (X, U, Y), where the auxiliary chain U models ...
Jérôme Lapuyade-Lahorgue +1 more
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Remote Sensing, 2019 Large amounts of farmland loss caused by urban expansion has been a severe global environmental problem. Therefore, monitoring urban encroachment upon farmland is a global issue.
Yuan Yuan +6 more
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Modeling non stationary hidden semi-markov chains with triplet markov chains and theory of evidence [PDF]
IEEE/SP 13th Workshop on Statistical Signal Processing, 2005, 2005 Hidden Markov chains, enabling one to recover the hidden process even for very large size, are widely used in various problems. On the one hand, it has been recently established that when the hidden chain is not stationary, the use of the theory of evidence is equivalent to consider a triplet Markov chain and can improve the efficiency of unsupervised ...
Wojciech Pieczynski
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Decoding and Re-estimation of fuzzy hidden semi Markov chain with observation dependent state
Journal of Industrial and Management Optimization, 2023 Z. Zararsız
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International Journal of Human-Computer InteractionBy implementing human-robot collaboration (HRC), cobots and operators can leverage their respective strengths during production to enhance efficiency and adaptability. However, coexistence with cobots can induce psychological stress in operators, potentially impairing their performance.
Wang, Kung-Jeng, Chen, Meng-Ping
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Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes
Entropy, 2022 Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time.
Sarah E. Marzen, James P. Crutchfield
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Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains [PDF]
, 2022 Hidden semi-Markov Models (HSMM's) - while broadly in use - are restricted to a discrete and uniform time grid. They are thus not well suited to explain often irregularly spaced discrete event data from continuous-time phenomena. We show that non-sampling-based latent state inference used in HSMM's can be generalized to latent Continuous-Time semi ...
Engelmann, Nicolai, Koeppl, Heinz
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A PROBABILITY MODEL FOR DROUGHT PREDICTION USING FUSION OF MARKOV CHAIN AND SAX METHODS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 Drought is one of the most powerful natural disasters which are affected on different aspects of the environment. Most of the time this phenomenon is immense in the arid and semi-arid area.
Y. Jouybari-Moghaddam +2 more
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Quantile hidden semi-Markov models for multivariate time series
Statistics and computing, 2022 This paper develops a quantile hidden semi-Markov regression to jointly estimate multiple quantiles for the analysis of multivariate time series. The approach is based upon the Multivariate Asymmetric Laplace (MAL) distribution, which allows to model the
Luca Merlo +3 more
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