Transition Path Theory for Markov Jump Processes
By Philipp Metzner, Christof Schutte, Eric Vanden-Eijnden. Multiscale Modeling & simulation, 2009
461 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Molecular Biology, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/7171288185e6ebe17d1782600b60adef6bea547f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
In Molecular Biology
- Frank No'e
- Hao Wu
- Vijay S. Pande
- G. de Fabritiis
- Fabian Paul
- Gregory R. Bowman
- Toni Giorgino
- G. Pérez-Hernández
In Statistical and Nonlinear Physics
- Vijay S. Pande
- Jascha Narain Sohl-Dickstein
- Frank No'e
- Mayur Mudigonda
- A. Berger
- Fabian Paul
- Michael Robert DeWeese
- Greg Yang
Citations in the corpus
- A Markov Jump Process for More Efficient Hamiltonian Monte Carlo (2015, background): “Work has also been done on efficient sampling of trajectories in Markov jump processes [5] and the statistical properties of these trajectories [6].”
- Markov models of molecular kinetics: generation and validation. (2011, unknown): “the ensemble of protein folding pathways [22, 50].”
- Identification of slow molecular order parameters for Markov model construction. (2013, background): “From this propagator, many properties can be calculated, such as transition pathways between two sets of configurations [51, 7, 67].”
- An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation (2014, background): “They can be both experimentally accessed via relaxation or correlation functions that are measurable with various spectroscopic techniques [2, 5, 20, 28], but can also be directly calculated from the Markov model eigenvalues as implied timescales, Eq.”