Reconciling Simulations and Experiments With BICePs: A Review
Vincent A. Voelz, Ge Yunhui, Robert M. Raddi
Abstract
Bayesian Inference of Conformational Populations (BICePs) is an algorithm developed to reconcile simulated ensembles with sparse experimental measurements. The Bayesian framework of BICePs enables population reweighting as a post-simulation processing step, with several advantages over existing methods, including the proper use of reference potentials, and the estimation of a Bayes factor-like quantity called the BICePs score for model selection. Here, we summarize the theory underlying this method in context with related algorithms, review the history of BICePs applications to date, and discuss current shortcomings along with future plans for improvement.
Topics & Concepts
Bayes' theoremContext (archaeology)Bayes factorInferenceComputer scienceBicepsBayesian inferenceBayesian probabilityPopulationMachine learningArtificial intelligenceEconometricsMathematicsBiologyMedicinePaleontologyEnvironmental healthAnatomyProtein Structure and DynamicsStatistical Methods and Bayesian InferenceGaussian Processes and Bayesian Inference