Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity

DOI10.1098/RSTA.2019.0345zbMath1470.62155arXiv2004.10586OpenAlexW3028020997WikidataQ95651806 ScholiaQ95651806MaRDI QIDQ5154215

Mark D. O'Neill, Caroline H. Roney, Steven A. Niederer, Steven E. Williams, Cesare Corrado, Daniel O'Hare, Sam Coveney, Jeremy E. Oakley, Richard D. Wilkinson, Richard H. Clayton

Publication date: 4 October 2021

Published in: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.10586

zbMATH Keywords

manifoldGaussian processstatisticsatrial fibrillationprobabilistic interpolationcardiac conduction velocitylocal activation timemedical computing

Mathematics Subject Classification ID

Gaussian processes (60G15) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Physiological flow (92C35)

Related Items (1)

Kernel-based system identification with manifold regularization: a Bayesian perspective

Cites Work

• Unnamed Item
• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
• Hilbert space methods for reduced-rank Gaussian process regression
• When is a truncated covariance function on the line a covariance function on the circle?

This page was built for publication: Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity