Results 261 to 270 of about 254,480 (307)
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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 +2 more
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Nature Methods, 2019
You can look back there to explain things, but the explanation disappears. You’ll never find it there. Things are not explained by the past. They’re explained by what happens now.
Jasleen K. Grewal +2 more
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You can look back there to explain things, but the explanation disappears. You’ll never find it there. Things are not explained by the past. They’re explained by what happens now.
Jasleen K. Grewal +2 more
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Journal of Statistical Physics, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Maes, Christian, van Wieren, Maarten H.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Maes, Christian, van Wieren, Maarten H.
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On Markov modelling of turbulence
Journal of Fluid Mechanics, 1994We consider Lagrangian stochastic modelling of the relative motion of two fluid particles in the inertial range of a turbulent flow. Eulerian analysis of such modelling corresponds to an equation for the Eulerian probability distribution of velocity-vector increments which introduces a hierarchy of constraints for making the model consistent with ...
PEDRIZZETTI, Gianni, NOVIKOV E. A.
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Modeling Markov sources and hidden Markov models by P systems
Journal of Membrane Computing, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 2002
The spatial distribution of gray level intensities in an image can be naturally modeled using Markov random field (MRF) models. We develop and investigate the performance of face detection algorithms derived from MRF considerations. For enhanced detection, the MRF models are defined for every permutation of site indices (pixels) in the image.
Sarat C. Dass, Anil K. Jain 0001
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The spatial distribution of gray level intensities in an image can be naturally modeled using Markov random field (MRF) models. We develop and investigate the performance of face detection algorithms derived from MRF considerations. For enhanced detection, the MRF models are defined for every permutation of site indices (pixels) in the image.
Sarat C. Dass, Anil K. Jain 0001
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Markov models — training and evaluation of hidden Markov models
Nature Methods, 2020“With one eye you are looking at the outside world, while with the other you are looking within yourself.” —Amedeo ...
Jasleen K. Grewal +2 more
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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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'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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2014
This section reviews the relation between the continuous dynamics of a molecular system in thermal equilibrium and the kinetics given by a Markov State Model (MSM). We will introduce the dynamical propagator, an error-less, alternative description of the continuous dynamics, and show how MSMs result from its discretization.
Marco, Sarich +2 more
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This section reviews the relation between the continuous dynamics of a molecular system in thermal equilibrium and the kinetics given by a Markov State Model (MSM). We will introduce the dynamical propagator, an error-less, alternative description of the continuous dynamics, and show how MSMs result from its discretization.
Marco, Sarich +2 more
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Usability analysis with Markov models
ACM Transactions on Computer-Human Interaction, 2001How hard to users to find interactive devices to use to achieve their goals, and how can we get this information early enough to influence design? We show that Markov modeling can obtain suitable measures, and we provide formulas that can be used for a large class of systems. We analyze and consider alternative designs for various real examples.
Harold W. Thimbleby +2 more
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