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Optimized monte carlo methods

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Advances in Computer Simulation

Part of the book series: Lecture Notes in Physics ((LNP,volume 501))

Abstract

I discuss optimized data analysis and Monte Carlo methods. Reweighting methods are discussed through examples, such as Lee-Yang zeroes in the Ising model and the absence of deconfinement in QCD. Reweighted data analysis and multihistogramming are also discussed. I introduce simulated tempering, and, as an example, its application to the random field Ising model. I illustrate parallel tempering, and discuss some crucial technical details such as thermalization and volume scaling. I give a general perspective by discussing umbrella methods and the multicanonical approach.

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Authors and Affiliations

  1. Dipartimento di Fisica, Università di Cagliari, via Ospedale 72, 09100, Cagliari, Italy

    Enzo Marinari

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  1. Enzo Marinari
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János Kertész Imre Kondor

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© 1998 Springer-Verlag

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Marinari, E. (1998). Optimized monte carlo methods. In: Kertész, J., Kondor, I. (eds) Advances in Computer Simulation. Lecture Notes in Physics, vol 501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0105459

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  • DOI: https://doi.org/10.1007/BFb0105459

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63942-8

  • Online ISBN: 978-3-540-69675-9

  • eBook Packages: Springer Book Archive

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