bayesImagesS: Bayesian methods for image segmentation using a hidden Potts model

ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.4225/09/584e37ae2a6b9&rft.title=bayesImagesS: Bayesian methods for image segmentation using a hidden Potts model&rft.identifier=10.4225/09/584e37ae2a6b9&rft.publisher=Queensland University of Technology&rft.description=R package, bayesimageS, implements Bayesian image analysis using the hidden Potts model with external field prior. Ltent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm and approximate Bayesian computation. This R package was written during a QUT based PhD, which was a collaborative project between QUT and the Radiation Oncology Mater Centre, Queensland Health titled, Bayesian computational methods for spatial analysis of images. It is an R source package (.tar.gz) containing.R and .cpp (R and C++) source code. &rft.creator=Moores, Matthew &rft.creator=Mengersen, Kerrie &rft.date=2016&rft.edition=1&rft.relation=10.1007/s11222-014-9525-6&rft.relation=10.1016/j.csda.2014.12.001&rft_rights=© Queensland University of Technology, 2016. &rft_rights=http://www.gnu.org/licenses/gpl-2.0.html&rft_subject=Image segmentation&rft_subject=Bayesian statistics&rft_subject=STATISTICS&rft_subject=MATHEMATICAL SCIENCES&rft.type=dataset&rft.language=English Access the data