Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology

Author:

Dhamala JwalaORCID,Arevalo Hermenegild J.,Sapp John,Horácek B. Milan,Wu Katherine C.,Trayanova Natalia A.,Wang Linwei

Funder

National Science Foundation

National Institutes of Health

National Heart, Lung, and Blood Institute

Pioneer Hi-Bred

Foundation for the National Institutes of Health

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference44 articles.

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2. An introduction to MCMC for machine learning;Andrieu;Mach. Learn.,2003

3. Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models;Arevalo;Nat. Commun.,2016

4. Brochu, E., Cora, V.M., De Freitas, N., 2010. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599.

5. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart a statement for healthcare professionals from the cardiac imaging committee of the council on clinical cardiology of the american heart association;Cerqueira;Circulation,2002

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