Eulerian Parameter Inference: a probabilistic change of variables for model-based inference with high-variability data sets
Vincent Wagner,
Benjamin Castellaz,
Lars Kaiser
et al.
Abstract:Advances in measurement technology have led to the generation of increasingly large data sets across various scientific fields. Data that capture the variability of the underlying system or process, such as single-cell or imaging data, are particularly interesting. However, calibrating computational models to explain this type of data remains challenging.We interpret the model calibration as a Stochastic Inverse Problem (SIP), where the measurements are interpreted as probabilistic samples. Our new SIP solutio… Show more
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