Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 11, Number 1 (2017), 891-915.
Estimating a smooth function on a large graph by Bayesian Laplacian regularisation
We study a Bayesian approach to estimating a smooth function in the context of regression or classification problems on large graphs. We derive theoretical results that show how asymptotically optimal Bayesian regularisation can be achieved under an asymptotic shape assumption on the underlying graph and a smoothness condition on the target function, both formulated in terms of the graph Laplacian. The priors we study are randomly scaled Gaussians with precision operators involving the Laplacian of the graph.
Electron. J. Statist. Volume 11, Number 1 (2017), 891-915.
Received: December 2016
First available in Project Euclid: 28 March 2017
Permanent link to this document
Digital Object Identifier
Kirichenko, Alisa; van Zanten, Harry. Estimating a smooth function on a large graph by Bayesian Laplacian regularisation. Electron. J. Statist. 11 (2017), no. 1, 891--915. doi:10.1214/17-EJS1253. https://projecteuclid.org/euclid.ejs/1490688317