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• W2075898357 abstract "The analysis of complex systems frequently poses the challenge to distinguish correlation from causation. Statistical physics has inspired very promising approaches to search for correlations in time series; the transfer entropy in particular (Hlavackova-Schindler et al., 2007). Now, methods from computational statistics can quantitatively assign significance to such correlation measures. In this study, we propose and apply a procedure to statistically assess transfer entropies by one-sided tests. We introduce to null models of vanishing correlations for time series with memory. We implemented them in an OpenMP-based, parallelized C++ package for multi-core CPUs. Using template meta-programming, we enable a compromise between memory and run time efficiency." @default.
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• W2075898357 date "2015-03-03" @default.
• W2075898357 modified "2023-10-03" @default.
• W2075898357 title "Efficient computation and statistical assessment of transfer entropy" @default.
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• W2075898357 doi "https://doi.org/10.3389/fphy.2015.00010" @default.
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