Accurate sunlight data is becoming essential for the clean-energy transition, but tracking how much solar radiation reaches ...

Abstract: To model sequentially observed multivariate nonstationary count data, we propose a switching Poisson-gamma dynamical systems (SPGDS), a dynamic probabilistic network with switching mechanism ...

Abstract: In this paper, a two-stage Bayesian learning-based approach is proposed to enable online dynamic security assessment (DSA) and preventive control in power systems. To develop a reliable data ...

Applied physics is the study of physics for a practical purpose, as opposed to physics motived solely for an improved fundamental understanding. This includes technological advances such as the ...

Learning from potential disinformation introduces specific cognitive biases, causing individuals to systematically deviate from an idealized Bayesian updating strategy.

Three Northwestern schools represented the University at the ACM Conference on Economics and Computation this month.

Every animal that has ever been studied closely, from the fruit fly to the philosopher, surrenders each day to a state that ...

In the midst of epidemics such as COVID-19, therapeutic candidates are unlikely to be able to complete the usual multiyear clinical trial and regulatory approval process within the course of an ...

Find out more about undergraduate study at the School of Electronic Engineering and Computer Science.

This viewpoint explores the mathematical foundations of DTs, including differential equations for health trajectory modeling, Bayesian networks for multiomics integration, Markov models for disease ...

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