4 papers · 1 filter
A Rigorous Theory of Conditional Mean Embeddings
Ilja Klebanov, Ingmar Schuster, T. J. Sullivan
Conditional mean embeddings (CMEs) have proven themselves to be a powerful tool in many machine learning applications. They allow the efficient conditioning of probability distribu…
Comments on the article "A Bayesian conjugate gradient method"
T. J. Sullivan
The recent article "A Bayesian conjugate gradient method" by Cockayne, Oates, Ipsen, and Girolami proposes an approximately Bayesian iterative procedure for the solution of a syste…
Geodesic analysis in Kendall's shape space with epidemiological applications
Esfandiar Nava-Yazdani, Hans-Christian Hege, T. J. Sullivan +1
We analytically determine Jacobi fields and parallel transports and compute geodesic regression in Kendall's shape space. Using the derived expressions, we can fully leverage the g…
Optimality Criteria for Probabilistic Numerical Methods
Chris. J. Oates, Jon Cockayne, Dennis Prangle +2
It is well understood that Bayesian decision theory and average case analysis are essentially identical. However, if one is interested in performing uncertainty quantification for…