Nonparametric Bayesian label prediction on a graph

Jarno Hartog and Harry van Zanten

Computational Statistics & Data Analysis, 2018, vol. 120, issue C, 111-131

Abstract: 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.

Keywords: Binary classification; Graph; Bayesian nonparametrics (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:120:y:2018:i:c:p:111-131

DOI: 10.1016/j.csda.2017.11.008

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