Calibrating Functional Parameters in the Ion Channel Models of Cardiac Cells

Matthew Plumlee, V. Roshan Joseph and Hui Yang

Journal of the American Statistical Association, 2016, vol. 111, issue 514, 500-509

Abstract: Computational modeling is a popular tool to understand a diverse set of complex systems. The output from a computational model depends on a set of parameters that are unknown to the designer, but a modeler can estimate them by collecting physical data. In the described study of the ion channels of ventricular myocytes, the parameter of interest is a function as opposed to a scalar or a set of scalars. This article develops a new modeling strategy to nonparametrically study the functional parameter using Bayesian inference with Gaussian process prior distributions. A new sampling scheme is devised to address this unique problem.

Date: 2016
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DOI: 10.1080/01621459.2015.1119695

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