PURPOSETo address the need for more accurate risk stratification models for cancer immuno-oncology, this study aimed to develop a machine-learned Bayesian network model (BNM) for predicting outcomes ...

Eriko Hoshino ¹², Ingrid van Putten ¹³, Wardis Girsang ⁴, Budy P. Resosudarmo ⁵, Satoshi Yamazaki ²³, A Bayesian belief network model for community-based coastal resource management in the Kei Islands ...

This research has been peer-reviewed. For more information on this research see: A Bayesian Network Model for Seismic Risk Analysis. Risk Analysis, 2021.

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 ...

A study of over 6,800 patients identifies key pathways driving severe asthma exacerbation risk, highlighting roles for ...

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

Using this model, we show how established Bayesian network methodology can be applied to: (1) form posterior marginal distributions of variables based on evidence, (2) simulate scenarios, (3) update ...

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 ...