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Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models
However, by sampling from an approximation of the exact posterior probability density function (pdf) of the parameters, the efficiency is gained at the expense of sampling accuracy.
High-dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model
The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models.
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