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Statistical inference in mechanistic models: time warping for improved gradient matching
2017
Computational statistics (Zeitschrift)
Inference in mechanistic models of non-linear differential equations is a challenging problem in current computational statistics. Due to the high computational costs of numerically solving the differential equations in every step of an iterative parameter adaptation scheme, approximate methods based on gradient matching have become popular. However, these methods critically depend on the smoothing scheme for function interpolation. The present article adapts an idea from manifold learning and
doi:10.1007/s00180-017-0753-z
pmid:31258254
pmcid:PMC6560940
fatcat:tfqtsuwv4bdg5pasdt2apx2f7u