1 code implementation • 7 Nov 2023 • Namu Kroupa, David Yallup, Will Handley, Michael Hobson
Using a fully Bayesian approach, Gaussian Process regression is extended to include marginalisation over the kernel choice and kernel hyperparameters.
1 code implementation • 13 Aug 2019 • Xi Chen, Farhan Feroz, Michael Hobson
We show through numerical examples that this Bayesian PR (BPR) method provides a very robust, self-adapting and computationally efficient `hands-off' solution to the problem of unrepresentative priors in Bayesian inference using NS.
1 code implementation • 12 Sep 2018 • Edward Higson, Will Handley, Michael Hobson, Anthony Lasenby
Our approach can also be readily applied to neural networks, where it allows the network architecture to be determined by the data in a principled Bayesian manner by treating the number of nodes and hidden layers as parameters.