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Cluster Sampling Methods

In: Monte Carlo Methods

Author

Listed:
  • Adrian Barbu

    (Florida State University, Department of Statistics)

  • Song-Chun Zhu

    (University of California, Los Angeles, Departments of Statistics and Computer Science)

Abstract

The Swendsen-Wang algorithm has been initially designed for addressing the critical slowing down in sampling the Ising and Potts models described below at or near the critical temperature where phase transitions occur. Fortuin and Kasteleyn [17] have mapped the Potts model to a percolation model [7]. The percolation model is a model for a porous material with randomly distributed pores though which a liquid can percolate. The model is defined on a set of nodes (e.g. organized on a lattice), each node having a label sampled independently from a Bernoulli random variable with expectation p, where label 1 represents a pore. Two adjacent nodes with both having label 1 are automatically connected by an edge. This way random clusters of nodes are obtained by sampling the node labels and automatically connecting adjacent nodes that have label 1.

Suggested Citation

  • Adrian Barbu & Song-Chun Zhu, 2020. "Cluster Sampling Methods," Springer Books, in: Monte Carlo Methods, chapter 6, pages 123-188, Springer.
    • Handle: RePEc:spr:sprchp:978-981-13-2971-5_6
    DOI: 10.1007/978-981-13-2971-5_6
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