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  "Title": "Bayesian Spatial Blind Source Separation",
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  "Authors@R": "c( person(\"Ben\", \"Wu\", \nemail = \"wuben@ruc.edu.cn\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Ying\", \"Guo\",\nemail = \"yguo2@emory.edu\",\nrole = \"aut\"),\nperson(\"Jian\", \"Kang\",\nemail = \"jiankang@umich.edu\",\nrole = \"aut\") )",
  "Description": "Gibbs sampling for Bayesian spatial blind source\nseparation (BSP-BSS). BSP-BSS is designed for spatially\ndependent signals in high dimensional and large-scale data,\nsuch as neuroimaging. The method assumes the expectation of the\nobserved images as a linear mixture of multiple sparse and\npiece-wise smooth latent source signals, and constructs a\nBayesian nonparametric prior by thresholding Gaussian\nprocesses. Details can be found in our paper: Wu et al. (2022+)\n\"Bayesian Spatial Blind Source Separation via the Thresholded\nGaussian Process\" <doi:10.1080/01621459.2022.2123336>.",
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  "Repository": "https://benwu233.r-universe.dev",
  "Date/Publication": "2022-11-25 02:01:16 UTC",
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