Search Results for author: Michael Hobson

Found 3 papers, 3 papers with code

Kernel-, mean- and noise-marginalised Gaussian processes for exoplanet transits and $H_0$ inference

1 code implementation7 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.

Gaussian Processes

Bayesian posterior repartitioning for nested sampling

1 code implementation13 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.

Bayesian Inference Unity

Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learning

1 code implementation12 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.

BIG-bench Machine Learning Computational Efficiency +1

Cannot find the paper you are looking for? You can Submit a new open access paper.