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  "Title": "Bayesian Methods for Image Segmentation using a Potts Model",
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  "Description": "Various algorithms for segmentation of 2D and 3D images,\nsuch as computed tomography and satellite remote sensing. This\npackage implements Bayesian image analysis using the hidden\nPotts model with external field prior of Moores et al. (2015)\n<doi:10.1016/j.csda.2014.12.001>. Latent labels are sampled\nusing chequerboard updating or Swendsen-Wang. Algorithms for\nthe smoothing parameter include pseudolikelihood, path\nsampling, the exchange algorithm, approximate Bayesian\ncomputation (ABC-MCMC and ABC-SMC), and the parametric\nfunctional approximate Bayesian (PFAB) algorithm. Refer to\nMoores, Pettitt & Mengersen (2020)\n<doi:10.1007/978-3-030-42553-1_6> for an overview and also to\n<doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for\nfurther details of specific algorithms.",
  "License": "GPL (>= 2) | file LICENSE",
  "URL": "https://bitbucket.org/Azeari/bayesimages,\nhttps://mooresm.github.io/bayesImageS/",
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