Abstract: In this study, train operations were modeled by Bayesian networks (BN), to use the probability essence of the BN to quantify their uncertainty (e.g., the epistemic and aleatoric uncertainty ...

Addressing this, this article introduces the gradient knowledge network based on the graph neural network’s message-passing mechanism within the variational Bayesian inference framework, which ...

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...

Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning ...

Graphs, maps and charts from The Times — and an invitation to students to discuss them live. What do you notice and wonder about the severity of drought in the U.S.? By The Learning Network ...

In 2030, it will range between $2.22 and $2.68, with an average price of $2.30. The Graph offers access to competitive and cost-efficient decentralized data sets. The network boasts a 99.99% uptime ...

Graphs are data structures that represent complex relationships across a wide range of domains, including social networks, knowledge bases, biological systems, and many more. In these graphs, entities ...

By The Learning Network A new collection of graphs, maps and charts organized by topic and type from our “What’s Going On in This Graph?” feature. By The Learning Network Want to learn ...