51 citations · 139 across the 8 of their papers we have counts for
8 papers
Statistical Inference for Generative Models with Maximum Mean Discrepancy
Francois-Xavier Briol, Alessandro Barp, Andrew B. Duncan +1
While likelihood-based inference and its variants provide a statistically efficient and widely applicable approach to parametric inference, their application to models involving in…
Hamiltonian Monte Carlo on Symmetric and Homogeneous Spaces via Symplectic Reduction
Alessandro Barp, Anthony Kennedy, Mark Girolami
The Hamiltonian Monte Carlo method generates samples by introducing a mechanical system that explores the target density. For distributions on manifolds it is not always simple to…
Discussion of "Sequential Quasi-Monte Carlo" by Mathieu Gerber and Nicolas Chopin
Chris. J. Oates, Daniel Simpson, Mark Girolami
A discussion on the possibility of reducing the variance of quasi-Monte Carlo estimators in applications. Further details are provided in the accompanying paper "Variance Reduction…
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…
The Controlled Thermodynamic Integral for Bayesian Model Comparison
Chris J. Oates, Theodore Papamarkou, Mark Girolami
Bayesian model comparison relies upon the model evidence, yet for many models of interest the model evidence is unavailable in closed form and must be approximated. Many of the est…