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 (6)

• Auto-Encoding Variational Bayes
• Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
• Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation
• Variational Inference using Implicit Distributions
• Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
• Reducing Reparameterization Gradient Variance

Cited by in corpus (1)

• Adversarial Variational Optimization of Non-Differentiable Simulators