Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Decision Analysis
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
An integrated risk measurement and optimization model for trustworthy software process management
Information Sciences: an International Journal
From the Editors---Games and Decisions in Reliability and Risk
Decision Analysis
| Hi-index | 0.00 |
The usefulness of graphical models in reasoning and decision making stems from facilitating four main computational features: (1) modular representation of probabilities, (2) systematic construction methods, (3) explicit encoding of independencies, and (4) efficient inference procedures. This note explains why the original introduction of influence diagrams, lacking formal underpinning of these features, has had only mild influence on automated reasoning research, and how Bayesian belief networks, which were formulated and defined directly by these features, became the focus of graphical modeling research.