Nonparametric Bayesian label prediction on a graph
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| Publication date | 2018 |
| Journal | Computational Statistics and Data Analysis |
| Volume | Issue number | 120 |
| Pages (from-to) | 111-131 |
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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. |
| Document type | Article |
| Language | English |
| Published at |
https://doi.org/10.1016/j.csda.2017.11.008
(Final published version)
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