Determination of normalizing constants for simulated tempering
نویسنده
چکیده
In this paper, we propose to estimate the normalizing constants for simulated tempering by a modified histogram algorithm, the so-called contour Monte Carlo algorithm, and compare the efficiency of simulated tempering and parallel tempering. Our analysis reveals that simulated tempering tends to mix faster than parallel tempering at low temperature levels for simulating from complex systems. The reason why simulated tempering is better than parallel tempering is discussed at length. r 2005 Elsevier B.V. All rights reserved. PACS: 02.70.Tt; 02.50.Ng
منابع مشابه
Sampling from multimodal distributions using tempered transitions
I present a new Markov chain sampling method appropriate for distributions with isolated modes. Like the recently-developed method of \simulated tempering", the \tempered transition" method uses a series of distributions that interpolate between the distribution of interest and a distribution for which sampling is easier. The new method has the advantage that it does not require approximate val...
متن کاملRandom Construction of Interpolating Sets for High-Dimensional Integration
Many high dimensional integrals can be reduced to the problem of finding the relative measures of two sets. Often one set will be exponentially larger than the other, making it difficult to compare the sizes. A standard method of dealing with this problem is to interpolate between the sets with a sequence of nested sets where neighboring sets have relative measures bounded above by a constant. ...
متن کاملMulticanonical MCMC for sampling rare events: an illustrative review
Multicanonical MCMC (Multicanonical Markov Chain Monte Carlo; Multicanonical Monte Carlo) is discussed as a method of rare event sampling. Starting from a review of the generic framework of importance sampling, multicanonical MCMC is introduced, followed by applications in random matrices, random graphs, and chaotic dynamical systems. Replica exchange MCMC (also known as parallel tempering or M...
متن کاملA Generalized Wang–Landau Algorithm for Monte Carlo Computation
Inference for a complex system with a rough energy landscape is a central topic in Monte Carlo computation. Motivated by the successes of the Wang–Landau algorithm in discrete systems, we generalize the algorithm to continuous systems. The generalized algorithm has some features that conventional Monte Carlo algorithms do not have. First, it provides a new method for Monte Carlo integration bas...
متن کاملStochastic Optimization Based Study of Dimerization Kinetics
1 Stochastic Optimization Based Study of Dimerization Kinetics Srijeeta Talukder, Shrabani Sen, Ralf Metzler, Suman K Banik, Pinaki Chaudhury Department of Chemistry, University of Calcutta, 92 A P C Road, Kolkata 700 009, India Institute for Physics & Astronomy, University of Potsdam, D-14476 Potsdam-Golm, Germany Physics Department, Tampere University of Technology, FI-33101 Tampere, Fi...
متن کامل