Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.
The results include a comparison between two different basis functions for temporal selectivity and how these generate different predictions for the dynamics of neural populations. The conclusions are ...
To enable more accurate estimation of connectivity, we propose a data-driven and theoretically grounded framework for optimally designing perturbation inputs, based on formulating the neural model as ...
Froot, K. A. "Consistent Covariance Matrix Estimation with Cross-Sectional Dependence and Heteroskedasticity in Cross-Sectional Financial Data." Journal of Financial and Quantitative Analysis 24, no.
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