Markov state models revisited: Principles and algorithms for unbiased observables | Paper Citation Record

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This paper cites Zuckerman.bRiteWeight: Randomized iterative reweighting for biased trajectory data.

Markov state models revisited: Principles and algorithms for unbiased observables Zuckerman.bRiteWeight: Randomized iterative reweighting for biased trajectory data

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This paper cites On the removal of initial state bias from simula- tion data.

Markov state models revisited: Principles and algorithms for unbiased observables On the removal of initial state bias from simula- tion data

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This paper cites On the Hill relation and the mean reaction time for metastable processes.

Markov state models revisited: Principles and algorithms for unbiased observables On the Hill relation and the mean reaction time for metastable processes

Reference 3

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This paper cites Beyond microscopic reversibility: Are observable nonequi- librium processes precisely reversible?.

Markov state models revisited: Principles and algorithms for unbiased observables Beyond microscopic reversibility: Are observable nonequi- librium processes precisely reversible?

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This paper cites On the relation between projections of the reweighted path ensemble.

Markov state models revisited: Principles and algorithms for unbiased observables On the relation between projections of the reweighted path ensemble

Reference 5

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This paper cites New York: Oxford University Press, 1987.isbn: 978-0-19-504277-1.url:https://openlibrary.org/books/OL2724292M/Introduction_ to_modern_statistical_mechanics.

Markov state models revisited: Principles and algorithms for unbiased observables New York: Oxford University Press, 1987.isbn: 978-0-19-504277-1.url:https://openlibrary.org/books/OL2724292M/Introduction_ to_modern_statistical_mechanics

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This paper cites Markov state models of biomolecular conformational dynamics.

Markov state models revisited: Principles and algorithms for unbiased observables Markov state models of biomolecular conformational dynamics

Reference 7

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This paper cites Probability distributions of molecular observables computed from Markov models. II. Uncertainties in observables and their time evolution.

Markov state models revisited: Principles and algorithms for unbiased observables Probability distributions of molecular observables computed from Markov models. II. Uncertainties in observables and their time evolution

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This paper cites Accelerated estimation of long-timescale kinetics from weighted ensemble simulation via non-Markovian “microbin.

Markov state models revisited: Principles and algorithms for unbiased observables Accelerated estimation of long-timescale kinetics from weighted ensemble simulation via non-Markovian “microbin

Reference 9

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This paper cites Analysis of the accelerated weighted ensemble methodology.

Markov state models revisited: Principles and algorithms for unbiased observables Analysis of the accelerated weighted ensemble methodology

Reference 10

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This paper cites Reaction path study of conformational transitions in flexible systems: Applications to peptides.

Markov state models revisited: Principles and algorithms for unbiased observables Reaction path study of conformational transitions in flexible systems: Applications to peptides

Reference 11

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This paper cites Separating forward and backward pathways in nonequilibrium umbrella sampling.

Markov state models revisited: Principles and algorithms for unbiased observables Separating forward and backward pathways in nonequilibrium umbrella sampling

Reference 12

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This paper cites Gardiner.Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences.

Markov state models revisited: Principles and algorithms for unbiased observables Gardiner.Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences

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This paper cites Optimized Markov state models for metastable systems.

Markov state models revisited: Principles and algorithms for unbiased observables Optimized Markov state models for metastable systems

Reference 14

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This paper cites MSMBuilder: Statistical models for biomolecular dynamics.

Markov state models revisited: Principles and algorithms for unbiased observables MSMBuilder: Statistical models for biomolecular dynamics

Reference 15

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This paper cites Hill.Free Energy Transduction and Biochemical Cycle Kinetics.

Markov state models revisited: Principles and algorithms for unbiased observables Hill.Free Energy Transduction and Biochemical Cycle Kinetics

Reference 16

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This paper cites Deeptime: A Python library for machine learning dynamical models from time series data.

Markov state models revisited: Principles and algorithms for unbiased observables Deeptime: A Python library for machine learning dynamical models from time series data

Reference 17

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This paper cites Markov state models: From an art to a science.

Markov state models revisited: Principles and algorithms for unbiased observables Markov state models: From an art to a science

Reference 18

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This paper cites Optimized parameter selection reveals trends in Markov state models for protein folding.

Markov state models revisited: Principles and algorithms for unbiased observables Optimized parameter selection reveals trends in Markov state models for protein folding

Reference 19

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This paper cites Randomized iterative trajectory reweighting for steady-state distributions without discretization error.

Markov state models revisited: Principles and algorithms for unbiased observables Randomized iterative trajectory reweighting for steady-state distributions without discretization error

Reference 20

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Observation 67cfd27e-0ced-443d-b18b-b5c0fa0155f9 · outbound

This paper cites Uncertainties in Markov state models of small proteins.

Markov state models revisited: Principles and algorithms for unbiased observables Uncertainties in Markov state models of small proteins

Reference 21

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This paper cites Probability distributions of molecular observables computed from Markov models.

Markov state models revisited: Principles and algorithms for unbiased observables Probability distributions of molecular observables computed from Markov models

Reference 23

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Reference 24

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This paper cites Markov state models from short non-equilibrium simulations—Analysis and cor- rection of estimation bias.

Markov state models revisited: Principles and algorithms for unbiased observables Markov state models from short non-equilibrium simulations—Analysis and cor- rection of estimation bias

Reference 25

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This paper cites Zuckerman.Regularized RiteWeight for sparse trajectory data: A smoothed stationary reweighting framework.

Markov state models revisited: Principles and algorithms for unbiased observables Zuckerman.Regularized RiteWeight for sparse trajectory data: A smoothed stationary reweighting framework

Reference 26

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This paper cites Everything you wanted to know about Markov state models but were afraid to ask.

Markov state models revisited: Principles and algorithms for unbiased observables Everything you wanted to know about Markov state models but were afraid to ask

Reference 27

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This paper cites Markov models of molecular kinetics: Generation and validation.

Markov state models revisited: Principles and algorithms for unbiased observables Markov models of molecular kinetics: Generation and validation

Reference 28

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This paper cites Iterative trajectory reweighting for estimation of equilibrium and non-equilibrium observables.

Markov state models revisited: Principles and algorithms for unbiased observables Iterative trajectory reweighting for estimation of equilibrium and non-equilibrium observables

Reference 30

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This paper cites Unbiased estimation of equilibrium, rates, and committors from Markov state model analysis.

Markov state models revisited: Principles and algorithms for unbiased observables Unbiased estimation of equilibrium, rates, and committors from Markov state model analysis

Reference 31

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This paper cites On the approximation quality of Markov state mod- els.

Markov state models revisited: Principles and algorithms for unbiased observables On the approximation quality of Markov state mod- els

Reference 32

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This paper cites Equilibrium distribution from distributed computing (simula- tions of protein folding).

Markov state models revisited: Principles and algorithms for unbiased observables Equilibrium distribution from distributed computing (simula- tions of protein folding)

Reference 33

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This paper cites PyEMMA 2: A software package for estimation, validation, and analysis of Markov models.

Markov state models revisited: Principles and algorithms for unbiased observables PyEMMA 2: A software package for estimation, validation, and analysis of Markov models

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This paper cites Identifying mechanistically distinct pathways in kinetic tran- sition networks.

Markov state models revisited: Principles and algorithms for unbiased observables Identifying mechanistically distinct pathways in kinetic tran- sition networks

Reference 35

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This paper cites Error analysis and efficient sampling in Markovian state models for molecular dynamics.

Markov state models revisited: Principles and algorithms for unbiased observables Error analysis and efficient sampling in Markovian state models for molecular dynamics

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This paper cites Accurate estimation of protein folding and unfolding times: Beyond Markov state models.

Markov state models revisited: Principles and algorithms for unbiased observables Accurate estimation of protein folding and unfolding times: Beyond Markov state models

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This paper cites Simultaneous computation of dynamical and equilibrium information using a weighted ensemble of trajectories.

Markov state models revisited: Principles and algorithms for unbiased observables Simultaneous computation of dynamical and equilibrium information using a weighted ensemble of trajectories

Reference 38

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This paper cites What Markov state models can and cannot do: Correlation versus path-based observables in protein-folding models.

Markov state models revisited: Principles and algorithms for unbiased observables What Markov state models can and cannot do: Correlation versus path-based observables in protein-folding models

Reference 39

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Observation dac41a02-1b6c-43a0-a0a8-00a31159b1c4 · outbound

This paper cites Describing protein folding kinetics by molecular dynamics simulations. 1. Theory.

Markov state models revisited: Principles and algorithms for unbiased observables Describing protein folding kinetics by molecular dynamics simulations. 1. Theory

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This paper cites Describing protein folding kinetics by molecular dynamics simulations. 2. Example applications to alanine dipeptide and aβ-hairpin peptide.

Markov state models revisited: Principles and algorithms for unbiased observables Describing protein folding kinetics by molecular dynamics simulations. 2. Example applications to alanine dipeptide and aβ-hairpin peptide

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This paper cites Estimation and uncertainty of reversible Markov models.

Markov state models revisited: Principles and algorithms for unbiased observables Estimation and uncertainty of reversible Markov models

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This paper cites Error breakdown and sensitivity analysis of dynamical quantities in Markov state models.

Markov state models revisited: Principles and algorithms for unbiased observables Error breakdown and sensitivity analysis of dynamical quantities in Markov state models

Reference 43

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This paper cites Exact rate calculations by trajectory parallelization and tilting.

Markov state models revisited: Principles and algorithms for unbiased observables Exact rate calculations by trajectory parallelization and tilting

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This paper cites Exploring energy landscapes.

Markov state models revisited: Principles and algorithms for unbiased observables Exploring energy landscapes

Reference 46

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This paper cites Adaptive Markov state model estimation using short reseeding trajectories.

Markov state models revisited: Principles and algorithms for unbiased observables Adaptive Markov state model estimation using short reseeding trajectories

Reference 47

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Observation e15f89d9-bffb-48ed-9d5f-3fcf4aefb1b5 · outbound

This paper cites Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations.

Markov state models revisited: Principles and algorithms for unbiased observables Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations

Reference 48

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This paper cites Zuckerman.“Proof ” of the Hill Relation Between Probability Flux and Mean First-Passage Time.

Markov state models revisited: Principles and algorithms for unbiased observables Zuckerman.“Proof ” of the Hill Relation Between Probability Flux and Mean First-Passage Time

Reference 49

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This paper cites Zuckerman.Counting is not enough: A weakness of MSMs inherited by RiteWeight.

Markov state models revisited: Principles and algorithms for unbiased observables Zuckerman.Counting is not enough: A weakness of MSMs inherited by RiteWeight

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This paper cites Zuckerman.Statistical Physics of Biomolecules: An Introduction.

Markov state models revisited: Principles and algorithms for unbiased observables Zuckerman.Statistical Physics of Biomolecules: An Introduction

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This paper cites A gentle introduction to the non-equilibrium physics of trajectories: Theory, algorithms, and biomolecular applications.

Markov state models revisited: Principles and algorithms for unbiased observables A gentle introduction to the non-equilibrium physics of trajectories: Theory, algorithms, and biomolecular applications

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