title: Application of Biased Metropolis Algorithms: From protons to proteins.
abstract: We show that sampling with a biased Metropolis scheme is essentially equivalent to using the heatbath algorithm. However, the biased Metropolis method can also be applied when an efficient heatbath algorithm does not exist. This is first illustrated with an example from high energy physics (lattice gauge theory simulations). We then illustrate the Rugged Metropolis method, which is based on a similar biased updating scheme, but aims at very different applications. The goal of such applications is to locate the most likely configurations in a rugged free energy landscape, which is most relevant for simulations of biomolecules.
authors.affiliation: Department of Physics, Florida State University, Tallahassee, FL 32306-4350, United States ; School of Computational Science, Florida State University, Tallahassee, FL 32306-4120, United States.
authors.forename: Alexei
authors.initials: A
authors.lastname: Bazavov
authors.affiliation: Department of Physics, Florida State University, Tallahassee, FL 32306-4350, United States ; School of Computational Science, Florida State University, Tallahassee, FL 32306-4120, United States.
authors.forename: Bernd A
authors.initials: BA
authors.lastname: Berg
authors.affiliation: Department of Physics, Florida State University, Tallahassee, FL 32306-4350, United States ; School of Computational Science, Florida State University, Tallahassee, FL 32306-4120, United States ; Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306-4380, United States.
authors.forename: Huan-Xiang
authors.initials: HX
authors.lastname: Zhou
keywords: Biophysics; Higher energy physics; Markov chain Monte Carlo