| Authors |
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| Publication date |
2018
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| Journal |
Computational Statistics and Data Analysis
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| Volume | Issue number |
120
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| Pages (from-to) |
111-131
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| Organisations |
-
Faculty of Science (FNWI) - Korteweg-de Vries Institute for Mathematics (KdVI)
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| 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.
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| Document type |
Article
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| Language |
English
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| Published at |
https://doi.org/10.1016/j.csda.2017.11.008
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| Other links |
https://www.scopus.com/pages/publications/85038109591
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