‘Go with the Winners’ Simulations View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2002

AUTHORS

Karl Heinz Hoffmann , Michael Schreiber , Peter Grassberger , Walter Nadler

ABSTRACT

We describe a general strategy for sampling configurations from a given distribution (Gibbs-Boltzmann or other). It is not based on the Metropolis concept of establishing a Markov process whose stationary state is the desired distribution. Instead, it builds weighted instances according to a biased distribution. If the bias is optimal, all weights are equal and importance sampling is perfect. If not, ‘population control’ is applied by cloning/killing configurations whose weight is too high/low. It uses the fact that nontrivial problems in statistical physics are high-dimensional. Consequently, instances are built up in many steps, and the final weight can be guessed at an early stage. In contrast to evolutionary algorithms, the cloning/killing is done in such a way that the desired distribution is strictly observed without simultaneously keeping a large population in computer memory. We apply this method (closely related to diffusion-type quantum Monte Carlo methods) to several problems of polymer statistics, population dynamics, and percolation. More... »

PAGES

169-190

References to SciGraph publications

Book

TITLE

Computational Statistical Physics

ISBN

978-3-642-07571-1
978-3-662-04804-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-662-04804-7_11

DOI

http://dx.doi.org/10.1007/978-3-662-04804-7_11

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1014218204


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