Nonparametric Bayesian label prediction on a graph

Jarno Hartog, Harry van Zanten*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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An implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs is described. A hierarchical Bayesian approach with a randomly scaled Gaussian prior is considered. The prior uses the graph Laplacian to take into account the underlying geometry of the graph. A method based on a theoretically optimal prior and a more flexible variant using partial conjugacy are proposed. Two simulated data examples and two examples using real data are used in order to illustrate the proposed methods.

Original languageEnglish
Pages (from-to)111-131
Number of pages21
JournalComputational Statistics and Data Analysis
Early online date6 Dec 2017
Publication statusPublished - Apr 2018


  • Bayesian nonparametrics
  • Binary classification
  • Graph


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