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Linguistic hidden Markov models
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03., 2004In this paper we develop a hidden Markov model (HMM), called the linguistic HMM (LHMM), suitable for processing sequences of fuzzy vectors. A fuzzy vector B is an n-tuple of fuzzy numbers. Since fuzzy numbers are often associated with linguistic terms, such as "small," "medium," etc., a fuzzy vector can also be called a linguistic vector.
Mihail Popescu +2 more
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A Hidden Markov Model for Seismocardiography
IEEE Transactions on Biomedical Engineering, 2017We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate,
Johan Wahlström +6 more
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2019
Hidden Markov models are tractable to capture long-term dependencies but intractable to compute the transition probabilities of higher-order process. We propose a neural hidden Markov models to compute the transition probabilities of higher-order hidden Markov model by a neural network and reduce the cost of computation.
Zuoquan Lin, Jiehu Song
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Hidden Markov models are tractable to capture long-term dependencies but intractable to compute the transition probabilities of higher-order process. We propose a neural hidden Markov models to compute the transition probabilities of higher-order hidden Markov model by a neural network and reduce the cost of computation.
Zuoquan Lin, Jiehu Song
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Hidden Markov Model with Markovian emission
Monte Carlo Methods and Applications, 2020Abstract In our paper [A. Nasroallah and K. Elkimakh, HMM with emission process resulting from a special combination of independent Markovian emissions, Monte Carlo Methods Appl. 23 2017, 4, 287–306] we have studied, in a first scenario, the three fundamental hidden Markov problems assuming that, given the hidden process, the observed ...
Karima Elkimakh, Abdelaziz Nasroallah
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Hidden Markov Models in Biology
2009Markov and Hidden Markov models (HMMs) are introduced using examples from linkage mapping and sequence analysis. In the course, the forward-backward, the Viterbi, the Baum-Welch (EM) algorithm, and a Metropolis sampling scheme are presented.
Vogl, Claus, Futschik, Andreas
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A modified hidden Markov model
Automatica, 2013This paper considers two discrete time, finite state processes X and Y. In the usual hidden Markov model X modulates the values of Y. However, the values of Y are then i.i.d. given X. In this paper a new model is considered where the Markov chain X modulates the transition probabilities of the second, observed chain Y.
John van der Hoek, Robert J. Elliott
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Modelling stabilograms with hidden Markov models
Journal of Medical Engineering & Technology, 2008Hidden Markov models are an effective computational method for modelling and interpreting digital signals of biological, as well as other, origin. In the current investigation, we explored whether hidden Markov models can be used to control and represent phenomena in human balance signals recorded from subjects standing on a force platform ...
J, Rasku +4 more
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On the Learnability of Hidden Markov Models
2002A simple result is presented that links the learning of hidden Markov models to results in complexity theory about nonlearnability of finite automata under certain cryptographic assumptions. Rather than considering all probability distributions, or even just certain specific ones, the learning of a hidden Markov model takes place under a distribution ...
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Hidden Markov Models with Confidence
2016We consider the problem of training a Hidden Markov Model HMM from fully observable data and predicting the hidden states of an observed sequence. Our attention is focused to applications that require a list of potential sequences as a prediction. We propose a novel method based on Conformal Prediction CP that, for an arbitrary confidence level $$1 ...
Giovanni Cherubin, Ilia Nouretdinov
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Evolving the structure of hidden Markov models
IEEE Transactions on Evolutionary Computation, 2006A genetic algorithm (GA) is proposed for finding the structure of hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful building blocks. The search through the space of HMM structures is combined with optimization of the emission and transition probabilities using the classic Baum ...
Kyoung-Jae Won +2 more
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