Multiple Markov transition matrix method: Obtaining the stationary probability distribution from multiple simulations
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Summary
The multiple Markov transition matrix method (MMMM), an algorithm by which to estimate the stationary probability distribution from independent multiple molecular dynamics simulations with different Hamiltonians, has an advantage with respect to the reasonable evaluation of the stationary probabilities even from nonequilibrium trajectories.
- Type
- article
- Published
- 2009-09-01
- Cited by
- 9
- References
- 32
- OpenAlex
- https://openalex.org/W1978963053
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:20699357
Keywords
Markov chain, Stochastic matrix, Statistical physics, Stationary distribution, Probability distribution
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Cited by
- Free energies from dynamic weighted histogram analysis using unbiased Markov state model.
- Multi-Scale Free Energy Landscape calculation method by combination of coarse-grained and all-atom models
- MuSTAR MD: multi-scale sampling using temperature accelerated and replica exchange molecular dynamics.
- Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states.
- Optimal Estimation of Free Energies and Stationary Densities from Multiple Biased Simulations
- Dynamic Histogram Analysis To Determine Free Energies and Rates from Biased Simulations.
- Markov State Models: From an Art to a Science.
- The Conveyor Belt Umbrella Sampling (CBUS) Scheme: Principle and Application to the Calculation of the Absolute Binding Free Energies of Alkali Cations to Crown Ethers.
- Unsupervised Learning Methods for Molecular Simulation Data
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