Nonparametric Bayesian label prediction on a graph

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....

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Bibliographic Details
Published in:Computational statistics & data analysis 2018-04, Vol.120, p.111-131
Main Authors: Hartog, Jarno, van Zanten, Harry
Format: Article
Language:English
Subjects:
Bayesian nonparametrics
Binary classification
Graph
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Summary: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.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2017.11.008