140 citations · 1.9k across the 86 of their papers we have counts for
8 papers · 1 filter
Hamiltonian Monte Carlo for Hierarchical Models
M. J. Betancourt, Mark Girolami
Hierarchical modeling provides a framework for modeling the complex interactions typical of problems in applied statistics. By capturing these relationships, however, hierarchical…
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)
Pseudo-Marginal Bayesian Inference for Gaussian Processes
Maurizio Filippone, Mark Girolami
The main challenges that arise when adopting Gaussian Process priors in probabilistic modeling are how to carry out exact Bayesian inference and how to account for uncertainty on m…
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…
On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods
Anne-Marie Lyne, Mark Girolami, Yves Atchadé +2
A large number of statistical models are "doubly-intractable": the likelihood normalising term, which is a function of the model parameters, is intractable, as well as the marginal…
Bayesian Solution Uncertainty Quantification for Differential Equations
Oksana A. Chkrebtii, David A. Campbell, Ben Calderhead +1
We explore probability modelling of discretization uncertainty for system states defined implicitly by ordinary or partial differential equations. Accounting for this uncertainty c…