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Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains
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 +7 more
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Large-scale dependent multiple testing via hidden semi-Markov models
Computational statistics (Zeitschrift), 2023Jiangzhou Wang, Pengfei Wang
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Inhomogeneous hidden semi-Markov models for incompletely observed point processes
Annals of the Institute of Statistical Mathematics, 2022Amina Shahzadi +3 more
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Analyzing nonstationary discrete sequences using hidden semi-Markov chains
1998Nous proposons d'analyser des séquences discrètes - éventuellement multivariées - à l'aide de semi-chaînes de Markov cachées. Une semi-chaîne de Markov cachée est composée d'un processus non-observable ou caché qui est une semi-chaîne de Markov et d'un processus observable discret multivarié.
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Hidden semi-Markov chains : a new tool for analyzing nonstationary discrete sequences
1998Spatial structure in plant architectures can be described as discrete sequences which are very often nonstationary. We propose to use hidden semi-Markov chains for analyzing samples of such sequences. Having chosen a family of hidden semi-Markov chains, it is possible to estimate parameters, check for goodness of fit of the data and then use the fitted
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The assembly, regulation and function of the mitochondrial respiratory chain
Nature Reviews Molecular Cell Biology, 2021Irene Vercellino, Leonid A Sazanov
exaly