104 citations · 532 across the 11 of their papers we have counts for
11 papers
Large-eddy simulations with ClimateMachine: a new open-source code for atmospheric simulations on GPUs and CPUs
Akshay Sridhar, Yassine Tissaoui, Simone Marras +11
We introduce ClimateMachine, a new open-source atmosphere modeling framework using the Julia language to be performance portable on central processing units (CPUs) and graphics pro…
Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem
Mathieu Besançon, Theodore Papamarkou, David Anthoff +4
Random variables and their distributions are a central part in many areas of statistical methods. The Distributions.jl package provides Julia users and developers tools for working…
On the Geometric Ergodicity of Hamiltonian Monte Carlo
Samuel Livingstone, Michael Betancourt, Simon Byrne +1
We establish general conditions under which Markov chains produced by the Hamiltonian Monte Carlo method will and will not be geometrically ergodic. We consider implementations wit…
Empirical AUC for evaluating probabilistic forecasts
Simon Byrne
Scoring functions are used to evaluate and compare partially probabilistic forecasts. We investigate the use of rank-sum functions such as empirical Area Under the Curve (AUC), a w…
Optimizing The Integrator Step Size for Hamiltonian Monte Carlo
M. J. Betancourt, Simon Byrne, Mark Girolami
Hamiltonian Monte Carlo can provide powerful inference in complex statistical problems, but ultimately its performance is sensitive to various tuning parameters. In this paper we u…
The Geometric Foundations of Hamiltonian Monte Carlo
M. J. Betancourt, Simon Byrne, Samuel Livingstone +1
Although Hamiltonian Monte Carlo has proven an empirical success, the lack of a rigorous theoretical understanding of the algorithm has in many ways impeded both principled develop…
Structural Markov graph laws for Bayesian model uncertainty
Simon Byrne, A. Philip Dawid
This paper considers the problem of defining distributions over graphical structures. We propose an extension of the hyper Markov properties of Dawid and Lauritzen [Ann. Statist. 2…
Discussion of "Geodesic Monte Carlo on Embedded Manifolds"
Simon Byrne, Mark Girolami, Persi Diaconis +9
Contributed discussion and rejoinder to "Geodesic Monte Carlo on Embedded Manifolds" (arXiv:1301.6064)
Langevin diffusions and the Metropolis-adjusted Langevin algorithm
Tatiana Xifara, Chris Sherlock, Samuel Livingstone +2
We provide a clarification of the description of Langevin diffusions on Riemannian manifolds and of the measure underlying the invariant density. As a result we propose a new posit…
Geodesic Monte Carlo on Embedded Manifolds
Simon Byrne, Mark Girolami
Markov chain Monte Carlo methods explicitly defined on the manifold of probability distributions have recently been established. These methods are constructed from diffusions acros…
Retrospective-prospective symmetry in the likelihood and Bayesian analysis of case-control studies
Simon P. J. Byrne, A. Philip Dawid
Prentice & Pyke (1979) established that the maximum likelihood estimate of an odds-ratio in a case-control study is the same as would be found by running a logistic regression: in…