Search Results for author:
Found 5 papers, 0 papers with code
Causal knowledge engineering: A case study from COVID-19
The unique challenges of the setting lead to experiments with the elicitation approach, and what emerged was a knowledge engineering method we call Causal Knowledge Engineering (CKE).
The practice of qualitative parameterisation in the development of Bayesian networks
Since the practice focuses on qualitative issues, despite being quantitative in nature, we call this step qualitative parameterisation and provide an outline of its role in the BN development process.
BARD: A structured technique for group elicitation of Bayesian networks to support analytic reasoning
In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty.
Latent Variable Discovery Using Dependency Patterns
The causal discovery of Bayesian networks is an active and important research area, and it is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data.
Proximity-Based Non-uniform Abstractions for Approximate Planning
In a deterministic world, a planning agent can be certain of the consequences of its planned sequence of actions.
