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  "Title": "Markov Chain Monte Carlo for Potts Models",
  "Author": "Charles J. Geyer <charlie@stat.umn.edu> and Leif Johnson\n<ltjohnson@google.com>",
  "Maintainer": "Charles J. Geyer <charlie@stat.umn.edu>",
  "Description": "Do Markov chain Monte Carlo (MCMC) simulation of Potts\nmodels (Potts, 1952, <doi:10.1017/S0305004100027419>), which\nare the multi-color generalization of Ising models (so, as as\nspecial case, also simulates Ising models). Use the\nSwendsen-Wang algorithm (Swendsen and Wang, 1987,\n<doi:10.1103/PhysRevLett.58.86>) so MCMC is fast. Do maximum\ncomposite likelihood estimation of parameters (Besag, 1975,\n<doi:10.2307/2987782>, Lindsay, 1988, <doi:10.1090/conm/080>).",
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    "twopixel",
    "twopixel.nonoverlap",
    "unpackPotts"
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      "title": "Cache calculated Canonical Statistics for Potts Models.",
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        "fourpixel.nonoverlap",
        "generate_t_cache",
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        "genfourpixel",
        "gengridcache",
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        "genthreebythree",
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        "ninepixel.nonoverlap",
        "singleton",
        "sixteenpixel.nonoverlap",
        "twopixel",
        "twopixel.nonoverlap"
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      "title": "Calculate Canonical Statistic for Potts Model",
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        "calc_t_full",
        "calc_t_innergrid"
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        "gr.composite.ll"
      ]
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    {
      "page": "image.raw",
      "title": "Plot Potts Model Data",
      "topics": [
        "image.raw"
      ]
    },
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      "page": "packPotts",
      "title": "Transform Potts Model Data",
      "topics": [
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        "packPotts",
        "unpackPotts"
      ]
    },
    {
      "page": "potts",
      "title": "Potts Models",
      "topics": [
        "potts",
        "potts.potts",
        "potts.raw"
      ]
    }
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      "created": "2014-04-01",
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