Transition path sampling is a method for estimating the rates of rare eventsin molecular systems based on the gradual transformation of a path distributioncontaining a small fraction of reactive trajectories into a biased distributionin which these rare trajectories have become frequent. Then, a multistatereweighting scheme is implemented to postprocess data collected from the stagedsimulations. Herein, we show how Bayes formula allows to directly construct abiased sample containing an enhanced fraction of reactive trajectories and toconcomitantly estimate the transition rate from this sample. The approach canremediate the convergence issues encountered in free energy perturbation orumbrella sampling simulations when the transformed distribution insufficientlyoverlaps with the reference distribution.
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