The Bayesian Artificial Intelligence research lab was established in late 2018, as part of the EPSRC Fellowship project “Bayesian Artificial Intelligence for Decision Making under Uncertainty”. The lab’s main research focuses on unsupervised machine learning algorithms for causal structure learning. Our work extends to learning causal Bayesian Networks (BNs) for prediction and optimal intervention/decision-making, including approaches that combine data with knowledge.
Broadly, the lab’s research activities include:
- Artificial Intelligence
- Bayesian Inference
- Causal Machine Learning
- Causal Discovery
- Knowledge-Based Systems
- Optimal decision-making
- Statistics and Probability Theory
- Uncertainty Quantification
We apply our research to a wide range of fields including medicine and healthcare, sports, finance, forensics, and gaming.
The lab has a close collaboration with the Risk Information Management research group, the Alan Turing Institute, and Agena Ltd, the UK company that develops the Bayesian risk and decision analysis software called AgenaRisk.