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of interacting particle methods for Bayesian inversion by including model error in the likelihood evaluation. As model problem, we will consider the inference of parameters in phenomenological models for cardiac
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infinite-dimensional (e.g., space-dependent) parameters and state variables. Inferring these parameters and/or states from large amounts of possibly high-resolution data leads to computationally intensive
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