Nature

Bayesian networks

For making probabilistic inferences, a graph is worth a thousand words. A Bayesian network is a graph in which nodes represent entities such as molecules or genes. Nodes that interact are connected by ...
Diginomica

Can Bayesian Networks provide answers when Machine Learning comes up short? It's a question of probabilities

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...
JSTOR Daily

Probabilistic Mapping of Descriptive Health Status Responses Onto Health State Utilities Using Bayesian Networks: An Empirical Analysis Converting SF-12 Into EQ-5D Utility ...

Background: As quality-adjusted life years have become the standard metric in health economic evaluations, mapping health-profile or disease-specific measures onto preference-based measures to obtain ...
JSTOR Daily

Applying Bayesian modelling to assess climate change effects on biofuel production

Climate Research, Vol. 40, No. 2/3, CR SPECIAL 20: Integrating analysis of regional climate change and response options (December 10, 2009), pp. 249-260 (12 pages) ABSTRACT:Socioeconomic and ...
Semiconductor Engineering

Memory Devices-Based Bayesian Neural Networks For Edge AI

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, ...
BMJ

427 Applying bayesian networks to injury occurrence in professional football

Background Bayesian networks (BN) are directed acyclic graphs derived from empirical data that describe the dependency and probability structure. It may facilitate understanding of complex ...