Results 261 to 270 of about 73,803 (311)

Utilizing profile hidden Markov model databases for discovering viruses from metagenomic data: a comprehensive review. [PDF]

Brief Bioinform
Yu R, Huang Z, Lam TYC, Sun Y.
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Markov models — hidden Markov models

Nature Methods, 2019
“Everything we see hides another thing, we always want to see what is hidden by what we see” — Rene ...
Jasleen K. Grewal, Martin Krzywinski, Naomi Altman   +2 more
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Nonstationary hidden Markov model

Signal Processing, 1995
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SIN, B, KIM, JH Kim, JinHyung
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Ergodicity of hidden Markov models

Mathematics of Control, Signals, and Systems, 2005
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Giovanni B. Di Masi, Lukasz Stettner
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Hidden Markov Models in Bioinformatics

Current Bioinformatics, 2007
Hidden Markov Models (HMMs) became recently important and popular among bioinformatics researchers, and many software tools are based on them. In this survey, we first consider in some detail the mathematical foundations of HMMs, we describe the most important algorithms, and provide useful comparisons, pointing out advantages and drawbacks.
Valeria De Fonzo, ALUFFI PENTINI, Filippo, PARISI, Valerio   +2 more
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Contextual Hidden Markov Models

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
Multiple works have proposed extensions of HMMs for handling variability. We focus here on the design of HMMs whose probability distribution on sequences depends on additional external variables that we call the context, which may stand for emotion features in speech recognition, physical features in gesture recognition, etc. We show experimentally the
Radenen, Mathieu, Artières, Thierry
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Hidden Markov models

Current Opinion in Structural Biology, 1996
'Profiles' of protein structures and sequence alignments can detect subtle homologies. Profile analysis has been put on firmer mathematical ground by the introduction of hidden Markov model (HMM) methods. During the past year, applications of these powerful new HMM-based profiles have begun to appear in the fields of protein-structure prediction and ...
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Partially hidden Markov models

IEEE Transactions on Information Theory, 1996
Summary: Partially hidden Markov models (PHMM) are introduced. They differ from the ordinary HMM's in that both the transition probabilities of the hidden states and the output probabilities are conditioned on past observations. As an illustration they are applied to black and white image compression where the hidden variables may be interpreted as ...
Søren Forchhammer, Jorma Rissanen
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On the structure of hidden Markov models

Pattern Recognition Letters, 2004
This paper investigates the effect of HMM structure on the performance of HMM-based classifiers. The investigation is based on the framework of graphical models, the diffusion of credits of HMMs and empirical experiments. Although some researchers have focused on determining the number of states, this study shows that the topology has a stronger ...
Karim T. Abou-Moustafa, Mohamed Cheriet, Ching Y. Suen   +2 more
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Filtering on hidden Markov models

IEEE Signal Processing Letters, 2000
In this letter, we propose a novel approach to adapt the hidden Markov model (HMM) parameters when the original feature vector sequences are transformed by a causal finite impulse response (FIR) filter. Our approach enables us to be free from the requirement of retraining the whole recognition parameters when the feature vectors are changed and makes ...
Nam Soo Kim, Dong Kook Kim
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