Markov Chain Monte Carlo Algorithms: Theory and Practice

We describe the importance and widespread use of Markov chain Monte Carlo (MCMC) algorithms, with an emphasis on the roles in which theoretical analysis can help with their practical implementation. In particular, we discuss how to achieve rigorous quantitative bounds on convergence to stationarity using the coupling method together with drift and minorisation conditions. We also discuss recent advances in the field of adaptive MCMC, where the computer iteratively selects from among many different MCMC algorithms. Such adaptive MCMC algorithms may fail to converge if implemented naively, but they will converge correctly if certain conditions such as Diminishing Adaptation are satisfied

Wang-Landau simulation of Gō model molecules

Gō-like models are one of the oldest protein modeling concepts in computational physics and have proven their value over and over for forty years. The essence of a Gō model is to define a native contact matrix for a well-defined low-energy polymer configuration, e.g., the native state in the case of proteins or peptides. Many different potential shapes and many different cut-off distances in the definition of this native contact matrix have been proposed and applied. We investigate here the physical consequences of the choice for this cut-off distance in the Gō models derived for a square-well tangent sphere homopolymer chain. For this purpose we are performing flat-histogram Monte Carlo simulations of Wang-Landau type, obtaining the thermodynamic and structural properties of such models over the complete temperature range. Differences and similarities with Gō models for proteins and peptides are discussed