Robust and highly scalable estimation of directional couplings from time-shifted signals
<|reference_start|>Robust and highly scalable estimation of directional couplings from time-shifted signals: The estimation of directed couplings between the nodes of a network from indirect measurements is a central methodological challenge in scientific fields such as neuroscience, systems biology and economics. Unfortunately, the problem is generally ill-posed due to the possible presence of unknown delays in the measurements. In this paper, we offer a solution of this problem by using a variational Bayes framework, where the uncertainty over the delays is marginalized in order to obtain conservative coupling estimates. To overcome the well-known overconfidence of classical variational methods, we use a hybrid-VI scheme where the (possibly flat or multimodal) posterior over the measurement parameters is estimated using a forward KL loss while the (nearly convex) conditional posterior over the couplings is estimated using the highly scalable gradient-based VI. In our ground-truth experiments, we show that the network provides reliable and conservative estimates of the couplings, greatly outperforming similar methods such as regression DCM.<|reference_end|>
@article{ambrogioni2024robust,
    title={Robust and highly scalable estimation of directional couplings from
  time-shifted signals},
    author={Luca Ambrogioni, Louis Rouillard, Demian Wassermann},
    journal={arXiv preprint arXiv:2406.02545},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02545},
    primaryClass={cs.LG}
}