Variational Inference over Non-differentiable Cardiac Simulators using Bayesian Optimization

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.

Workshops on Deep Learning for Physical Sciences and Machine Learning 4 Health, NIPS 2017

References in corpus (2)

• Variational Inference using Implicit Distributions
• Reducing Reparameterization Gradient Variance