Influence Diagrams, Belief Nets and Decision Analysis

Partial table of contents:

MODEL FORMULATION AND ANALYSIS.

From Influence to Relevance to Knowledge (R. Howard).

Complexity, Calibration and Causality in Influence Diagrams (T.Speed).

THEORETICAL FOUNDATIONS.

Statistical Principles on Graphs (J. Smith).

Influence and Belief Adjustment (M. Goldstein).

PROBLEMS AND APPLICATIONS: INDUSTRIAL.

Real Time Influence Diagrams for Monitoring and ControllingMechanical Systems (A. Agonino & K. Ramamurthi).

A Socio-technical Approach to Assessing Human Reliability (L.Phillips, et al.).

Bayesian Updating of Event Tree Parameters to Predict High RiskIncidents (R. Oliver & H. Yang).

PROBLEMS AND APPLICATIONS: MEDICAL.

EFFICIENCY AND COMPUTATIONAL ISSUES.

Towards Efficient Probabilistic Diagnosis in Multiply ConnectedBelief Networks (M. Henrion).

Towards Better Assessment and Sensitivity Procedures (R.Korsan).

Summary Observations (R. Howard).

Glossary.

Index.