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Welcome to BayesMBAR!

BayesMBAR stands for Bayesian Multistate Bennett Acceptance Ratio methods. It is a Bayesian generalization of the Multistate Bennett Acceptance Ratio (MBAR) method.

MBAR is useful for computing free energy differences among multiple thermodynamic states using configurations sampled from each state. The sampling can be performed using any method that can generate configurations according to the Boltzmann distribution, such as molecular dynamics simulations or Monte Carlo methods. MBAR can be used as a drop-in replacement for the weighted histogram analysis method (WHAM) and provides extra flexibilities. It is widely used in the field of computational chemistry and physics for calculating free energies. Example applications include computing the potential of mean force along a reaction coordinate, calculating hydration free energies of small molecules, and estimating binding free energies of ligands to proteins.

BayesMBAR generalizes the MBAR method by providing a Bayesian framework, where the original MBAR method is a special case. The advantage of the BayesMBAR method is two-fold:

  1. It provides more accurate uncertainty estimates for the free energy differences.
  2. It provides a principled way to incorporate prior knowledge to improve the accuracy of the free energy estimates. When the prior is chosen to be non-informative, BayesMBAR reduces to MBAR.