The problem of reconstructing nonlinear and complex dynamical systems from measured
data or time series is central to many scientific disciplines including physical, biological …

A nonlinear data assimilation technique is applied to determine and track effective
connections between ensembles of cultured spinal cord neurons measured with …

It has been recognized that many complex dynamical systems in the real world require a
description in terms of multiplex networks, where a set of common, mutually connected …

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of
previous works, to fully reconstruct the network structure from observed binary data remains …

Compressed sensing is a scheme that allows for sparse signals to be acquired, transmitted,
and stored using far fewer measurements than done by conventional means employing the …

Given a complex geospatial network with nodes distributed in a two-dimensional region of
physical space, can the locations of the nodes be determined and their connection patterns …

We consider the task of learning the structure of the graph underlying a mutually-exciting
multivariate Hawkes process in the high-dimensional setting. We propose a simple and …

In the real world there are situations where the network dynamics are transient (eg, various
spreading processes) and the final nodal states represent the available data. Can the …

We develop a completely data-driven approach to reconstructing coupled neuronal
networks that contain a small subset of chaotic neurons. Such chaotic elements can be the …

Learning healthcare delivery network with longitudinal electronic health records data Page 1
The Annals of Applied Statistics 2024, Vol. 18, No. 1, 882–898 https://doi.org/10.1214/23-AOAS1818 …