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Updated: Jun 16, 2026
10:56
An Unbiased Approach of Sampling TEM Sections in Neuroscience
Published on: April 13, 2019
The "weighted ensemble" path sampling method is statistically exact for a broad class of stochastic processes and
Bin W Zhang1, David Jasnow, Daniel M Zuckerman
1Department of Computational Biology, School of Medicine, University of Pittsburgh, Pennsylvania 15260, USA.
The Journal of Chemical Physics
|February 9, 2010
Summary
The weighted ensemble method provides statistically exact path sampling for rare events in complex dynamics. This rigorous approach simplifies to resampling in path space, validating adaptive binning strategies.
Area of Science:
- Computational physics
- Statistical mechanics
- Biophysics
Background:
- Rare events in complex systems are challenging to simulate.
- The weighted ensemble method is a rigorous approach for path sampling.
- Existing discussions on the method's validity are expanded.
Purpose of the Study:
- To demonstrate the statistical exactness of the weighted ensemble method.
- To show its applicability to Markovian and non-Markovian dynamics.
- To validate the use of nonstatic binning procedures.
Main Methods:
- Path-integral (path probability) formulation.
- Recasting the weighted ensemble method as resampling in path space.
- Numerical simulations to confirm theoretical claims.
Main Results:
- The weighted ensemble method is statistically exact for a broad range of dynamics.
- Nonstatic binning procedures are validated as guides for resampling.
- Adaptive binning successfully located target states in a model system.
Conclusions:
- The weighted ensemble method is a robust and versatile tool for rare event simulation.
- Resampling in path space offers a clear conceptual framework.
- Adaptive binning enhances the efficiency of rare event sampling.
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