Calibrating Functional Parameters in the Ion Channel Models of Cardiac Cells

Matthew Plumlee, V. Roshan Joseph, Hui Yang

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)500-509
Number of pages10
JournalJournal of the American Statistical Association
Volume111
Issue number514
DOIs
StatePublished - Apr 2 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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