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dc.contributor.authorSyring, Nicholas
Li, Meng
dc.date.accessioned 2018-02-26T17:22:11Z
dc.date.available 2018-02-26T17:22:11Z
dc.date.issued 2017
dc.identifier.citation Syring, Nicholas and Li, Meng. "BayesBD: An R Package for Bayesian Inference on Image Boundaries." The R Journal, 9, no. 2 (2017) The R Foundation: 149-162. https://hdl.handle.net/1911/99292.
dc.identifier.urihttps://hdl.handle.net/1911/99292
dc.description.abstract We present the BayesBD package providing Bayesian inference for boundaries of noisy images. The BayesBD package implements flexible Gaussian process priors indexed by the circle to recover the boundary in a binary or Gaussian noised image. The boundary recovered by BayesBD has the practical advantages of guaranteed geometric restrictions and convenient joint inferences under certain assumptions, in addition to its desirable theoretical property of achieving (nearly) minimax optimal rate in a way that is adaptive to the unknown smoothness. The core sampling tasks for our model have linear complexity, and are implemented in C++ for computational efficiency using packages Rcpp and RcppArmadillo. Users can access the full functionality of the package in both the command line and the corresponding shiny application. Additionally, the package includes numerous utility functions to aid users in data preparation and analysis of results. We compare BayesBD with selected existing packages using both simulations and real data applications, demonstrating the excellent performance and flexibility of BayesBD even when the observation contains complicated structural information that may violate its assumptions.
dc.language.iso eng
dc.publisher The R Foundation
dc.relation.urihttps://journal.r-project.org/archive/2017/RJ-2017-052/index.html
dc.rights This article is licensed under a Creative Commons Attribution 4.0 International license.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title BayesBD: An R Package for Bayesian Inference on Image Boundaries
dc.type Journal article
dc.citation.journalTitle The R Journal
dc.citation.volumeNumber 9
dc.citation.issueNumber 2
dc.identifier.digital RJ-2017-052
dc.type.dcmi Text
dc.type.publication publisher version
dc.citation.firstpage 149
dc.citation.lastpage 162


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