Variational Inference over Non-differentiable Cardiac Simulators using Bayesian Optimization
Machine Learning
2017-12-12 v1
Abstract
Performing inference over simulators is generally intractable as their runtime means we cannot compute a marginal likelihood. We develop a likelihood-free inference method to infer parameters for a cardiac simulator, which replicates electrical flow through the heart to the body surface. We improve the fit of a state-of-the-art simulator to an electrocardiogram (ECG) recorded from a real patient.
Cite
@article{arxiv.1712.03353,
title = {Variational Inference over Non-differentiable Cardiac Simulators using Bayesian Optimization},
author = {Adam McCarthy and Blanca Rodriguez and Ana Minchole},
journal= {arXiv preprint arXiv:1712.03353},
year = {2017}
}
Comments
Workshops on Deep Learning for Physical Sciences and Machine Learning 4 Health, NIPS 2017