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Nonparametric Bayesian label prediction on a graph
lib:348da9d0e63cab62 (v1.0.0)
Authors: Jarno Hartog,Harry van Zanten
ArXiv: 1612.01930
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1612.01930v2
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.
ArXiv: 1612.01930
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1612.01930v2
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.
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