One of the primary reasons for building a statistical model is to make predictions with it. While S-Plus is a proven tool for building models, its facilities for predicition are limited to small models. A complex risk analysis or decision analysis problem may require stringing several smaller models (fit in S-Plus) together to make a complete prediction. For example, a reliability model for a complex system is built from smaller models for the subsystems and components. Furthermore, a good analyst does not make a single prediction, but rather explores the implications of critical assumptions made in the modelling. Although an analyst could program S-Plus to do all these tasks, S-Plus does not support them directly.

Graphical-Belief is tool for exploring the predictive aspects of models. It is based on the technology of graphical models (also known as influence diagrams, belief networks or Bayes nets) which has already become a standard in decision analysis, statistics and artificial intelligence. These models have been used successfully in such diverse areas as system level reliability, medical decision making, financial planning and operations managment. Graphical-Belief is a complete environment for building and exploring risk models. It provides a wide range of tools for modelling tasks including:

The model graph takes as complex task and breaks it up into small pieces. The modeller only specifies the direct interactions between variables in the model; Graphical-Belief calculates all the implied dependencies between remote variables.
Knowledge Engineering
A large quantity of knowledge from many sources (both expert opinion and data) go into a typical graphical model. Graphical-Belief provides tools for maintaining that knowledge. Modellers can draw from a library of previously built knowledge fragments and generic knowledge structures. Because Graphical-Belief uses an object-oriented schema for storing knowledge, a change to a single prototype rule or variable is quickly propagated to many instances kept in the model.
Using Graphical-Belief, modellers have a choice of representation for relationships between variables: probability (for uncertain relationships), logic (for certain relationship) and belief functions (for imprecise uncertain relationships). Graphical-Belief achieves this flexibility with a generic inference engine which can be simply expanded to include other representations for relationships (including utilities and possibilities).
Dynamic Visualization
Graphical-Belief lets the analyst directly manipulate the model, exploring the implications of hypothetical scenarios and assessing the sensitivity of key predicitions to critial assumptions. Graphical-Belief has an very flexible parameter system which allows the analyst to easily study the sensitivity of a single parameter which impacts the model in many places.

Look at some examples of Graphical-Belief in action.

View a list of Graphical-Belief in publications and downloadable technical reports.

The Graphical-Belief user interface is implemented in Garnet.

Get more information about obtaining Graphical-Belief (and why it is not generally available).

get the home page for Russell Almond , author of Graphical-Belief.

Click here to get to the home page for Insightful (the company that StatSci has eventually evolved into).


I would like to thank David Madigan for his advice and collaboration on the project (he also sponsored the original posting of these pages).

The original Belief project was supported by Army Research Contract DAAL03-86K-0042) at Harvard University, Arthur Dempster, Principle Investigator. The design of Graphical-Belief is based in part on that work.

The Graphical-Belief program at StatSci has been partially supported by the following grants:

Russell Almond, <lastname> (at)
Last modified: Fri Aug 16 14:28:35 1996