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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 ...
Naomi Altman +2 more
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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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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.
Naomi Altman +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.
Naomi Altman +2 more
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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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2014
When accurately estimated and validated, Markov model transition matrices contain information of the long-time molecular kinetics and thermodynamic properties of the molecular system studied, approximated on the discrete state space. Thus, many quantities of interest to the molecular scientist can now be calculated from the Markov model transition ...
Frank Noé, Jan-Hendrik Prinz
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When accurately estimated and validated, Markov model transition matrices contain information of the long-time molecular kinetics and thermodynamic properties of the molecular system studied, approximated on the discrete state space. Thus, many quantities of interest to the molecular scientist can now be calculated from the Markov model transition ...
Frank Noé, Jan-Hendrik Prinz
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2009
Publisher Summary This chapter explains the Markov processes and how one can use them to model many phenomena, including human behavior. It also defines how to build a hidden Markov model to characterize the spiking behavior of a neuron. A Markov model describes a system as a set of discrete states and transition probabilities of moving from any one ...
Michael E. Lusignan +5 more
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Publisher Summary This chapter explains the Markov processes and how one can use them to model many phenomena, including human behavior. It also defines how to build a hidden Markov model to characterize the spiking behavior of a neuron. A Markov model describes a system as a set of discrete states and transition probabilities of moving from any one ...
Michael E. Lusignan +5 more
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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.
Jan-Hendrik Prinz +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.
Jan-Hendrik Prinz +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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Computer Graphics and Image Processing, 1980
Publisher Summary This chapter reviews the Markov Mesh models as originally given in the works of Abend, Harley, and Kanal. It also presents some inputs on some related references and developments of Markov Random Fields (MRF) models. The Markov Mesh models presented in the works of these authors sought to incorporate spatial dependence in reducing ...
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Publisher Summary This chapter reviews the Markov Mesh models as originally given in the works of Abend, Harley, and Kanal. It also presents some inputs on some related references and developments of Markov Random Fields (MRF) models. The Markov Mesh models presented in the works of these authors sought to incorporate spatial dependence in reducing ...
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Generalized Markov Models [PDF]
, 1999 The main focus of this book is to study systems that evolve randomly in time. We encountered several applications in Chapter 2 where the system is observed at time n = 0, 1, 2, 3,.... In such cases, we define X n as the state of the system at time n and study the discrete-time stochastic process {X n, n ≥ 0}.
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