- Review of the 2014 BayesiaLab User Conference
- Judea Pearl
From Bayesian Networks to Causal and Counterfactual Reasoning - Lionel Jouffe
Causal Analysis with Structural Equation Models and Bayesian Networks - Alex Cosmas
So you can predict the future... But can you change it? - Mick McWilliams
Using Bayesian Networks to Model Key Drivers - David Aebischer
Michael Grimes
Bayesian Belief Network Applications for Supporting Warfighters - Ferit Akova
ProvenCare Lumbar Spine Prediction Model - Jack Y. Chen
Revolutionizing Decision Making: How Analytics Will Take Over the Business - Roman Fomin
Prediction of Overall Team Peformance and Injury in Team Sports - Christina Ray
Intelligence Tradecraft and Bayesian Models - Michael Ryall
When nodes think: using BayesiaLab to analyze decisions in game theoretic settings
2014 BayesiaLab User Conference Presentations
So you can predict the future... But can you change it?
Alex Cosmas
Chief Scientist, Booz Allen Hamilton
Recorded on September 24, 2014, at UCLA.
The analytics community has invested significant resources in developing effective predictive analytical methods. However, even the most accurate predictive forecasts have limited value unless they can also provide clear action steps to bring about desired results. Bayesian Belief Networks (BBNs) produce accurate predictive forecasts, but with appropriate modeler input are also able to identify causal relationships between variables and pinpoint drivers of desired targets. With causal relationships identified, BBNs may be used in a prescriptive fashion in order to make actionable decisions.
We present a BBN case study in the aviation space which identifies causal drivers of daily flight operations on flight delays and allows us to prescribe delay-reduction plans by acting on controllable drivers.