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Monte Carlo Methods pp 71–96Cite as

Metropolis Methods and Variants

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Abstract

In this chapter several variations of the original algorithm are discussed and the concepts of reversible jumps and diffusion are covered. Applications include simple image segmentation, furniture arrangement, and people counting.

Nicholas Metropolis seated with the MANIAC computer in the background.

“Most of us have grown so blase about computer developments and capabilities—even some that are spectacular—that it is difficult to believe or imagine there was a time when we suffered the noisy, painstakingly slow, electromechanical devices that chomped away on punched cards”

– Nicholas Metropolis

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Notes

  1. 1.

    http://homepages.inf.ed.ac.uk/rbf/CAVIAR/

  2. 2.

    http://www.cvg.cs.rdg.ac.uk/VSPETS/vspets-db.html

  3. 3.

    We use the term “vertices” instead of “symbols” (in the traditional definition of PCFG) to be consistent with the notations in graphical models.

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Authors and Affiliations

  1. Department of Statistics, Florida State University, Tallahassee, FL, USA

    Adrian Barbu

  2. Departments of Statistics and Computer Science, University of California, Los Angeles, Los Angeles, CA, USA

    Song-Chun Zhu

Authors
  1. Adrian Barbu
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  2. Song-Chun Zhu
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© 2020 Springer Nature Singapore Pte Ltd.

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Barbu, A., Zhu, SC. (2020). Metropolis Methods and Variants. In: Monte Carlo Methods. Springer, Singapore. https://doi.org/10.1007/978-981-13-2971-5_4

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