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Reweighting from the mixture distribution as a better way to describe the Multistate Bennett Acceptance Ratio
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The multistate Bennett Acceptance Ratio is provably the lowest variance unbiased estimator of both free energies and ensemble averages, and has a number of important advantages over previous methods, such as WHAM. Despite its advantages, the original MBAR paper was rather dense and mathematically complicated, limiting the extent to which people could expand and apply it. We present here a different way to think about MBAR that is much more intuitive and makes it clearer why the method works so well.
Forward citations
Cited by 2 Pith papers
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Rare Event Analysis of Large Language Models
Using annealed transition path sampling plus MBAR reweighting, the authors estimate TinyStories-8M completion probabilities for extreme ARI and log-probability values that are unobservable by direct sampling.
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Machine learning assisted canonical sampling (MLACS)
MLACS is a production Python package that iteratively trains linear MLIP surrogates with active learning and MBAR reweighting to sample the DFT canonical ensemble at 50 to 100 times lower DFT cost.
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