Bayesian networks are graphical models that help understand and reason about complex systems with uncertainty using directed graphs.

Bayesian networks are increasingly important for integrating biological data and for inferring cellular networks and pathways. What are Bayesian networks and how are they used for inference?

Bayesian networks are widely used in genetic analysis, integration of biological data and modeling signaling pathways 1, 2.

Pedro Zorrilla ¹, Gema Carmona ², África De la Hera ³, Consuelo Varela-Ortega ², Pedro Martínez-Santos ¹, John Bromley ⁴, Hans Jorgen Henriksen ⁵, Evaluation of Bayesian Networks in Participatory ...

Explore Bayesian Networks, their principles, applications, and impact on AI and probabilistic reasoning with AI Terminologies 101.

A new technical paper titled “Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks” was published by researchers at Université Grenoble Alpes, CEA, ...

A novel Bayesian Hierarchical Network Model (BHNM) is designed for ensemble predictions of daily river stage, leveraging the spatial interdependence of river networks and hydrometeorological variables ...

For the first time, researchers from the University of Tokyo used a special kind of artificial intelligence called a Bayesian neural network to probe a dataset on gut bacteria in order to find ...