Search Results for author: Will Handley

Found 13 papers, 12 papers with code

Improving Gradient-guided Nested Sampling for Posterior Inference

1 code implementation6 Dec 2023 Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, Laurence Perreault-Levasseur

We present a performant, general-purpose gradient-guided nested sampling algorithm, ${\tt GGNS}$, combining the state of the art in differentiable programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization.

Clustering

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

Piecewise Normalizing Flows

1 code implementation4 May 2023 Harry Bevins, Will Handley, Thomas Gessey-Jones

We demonstrate the performance of the piecewise flows using some standard benchmarks and compare the accuracy of the flows to the approach taken in Stimper et al. (2022) for modelling multi-modal distributions.

Nested sampling with any prior you like

1 code implementation24 Feb 2021 Justin Alsing, Will Handley

In this letter we show that parametric bijectors trained on samples from a desired prior density provide a general-purpose method for constructing transformations from the uniform base density to a target prior, enabling the practical use of nested sampling under arbitrary priors.

Astronomy

CosmoBit: A GAMBIT module for computing cosmological observables and likelihoods

1 code implementation7 Sep 2020 The GAMBIT Cosmology Workgroup, :, Janina J. Renk, Patrick Stöcker, Sanjay Bloor, Selim Hotinli, Csaba Balázs, Torsten Bringmann, Tomás E. Gonzalo, Will Handley, Sebastian Hoof, Cullan Howlett, Felix Kahlhoefer, Pat Scott, Aaron C. Vincent, Martin White

We introduce $\sf{CosmoBit}$, a module within the open-source $\sf{GAMBIT}$ software framework for exploring connections between cosmology and particle physics with joint global fits.

Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology

Strengthening the bound on the mass of the lightest neutrino with terrestrial and cosmological experiments

1 code implementation7 Sep 2020 The GAMBIT Cosmology Workgroup, :, Patrick Stöcker, Csaba Balázs, Sanjay Bloor, Torsten Bringmann, Tomás E. Gonzalo, Will Handley, Selim Hotinli, Cullan Howlett, Felix Kahlhoefer, Janina J. Renk, Pat Scott, Aaron C. Vincent, Martin White

We determine the upper limit on the mass of the lightest neutrino from the most robust recent cosmological and terrestrial data.

Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology

Compromise-free Bayesian neural networks

1 code implementation25 Apr 2020 Kamran Javid, Will Handley, Mike Hobson, Anthony Lasenby

We conduct a thorough analysis of the relationship between the out-of-sample performance and the Bayesian evidence (marginal likelihood) of Bayesian neural networks (BNNs), as well as looking at the performance of ensembles of BNNs, both using the Boston housing dataset.

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

Diagnostic Tests for Nested Sampling Calculations

3 code implementations16 Apr 2018 Edward Higson, Will Handley, Mike Hobson, Anthony Lasenby

Nested sampling is an increasingly popular technique for Bayesian computation - in particular for multimodal, degenerate and high-dimensional problems.

Computation Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability

Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation

2 code implementations11 Apr 2017 Edward Higson, Will Handley, Mike Hobson, Anthony Lasenby

We introduce dynamic nested sampling: a generalisation of the nested sampling algorithm in which the number of "live points" varies to allocate samples more efficiently.

Computation Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability Methodology

Sampling Errors in Nested Sampling Parameter Estimation

2 code implementations28 Mar 2017 Edward Higson, Will Handley, Mike Hobson, Anthony Lasenby

Sampling errors in nested sampling parameter estimation differ from those in Bayesian evidence calculation, but have been little studied in the literature.

Methodology Instrumentation and Methods for Astrophysics Applications

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