Publications and On-Line Technical reports related to GRAPHICAL-BELIEF

This file contains a list of publications and technical reports releated to the Graphical-Belief project. As many of them were never published except in a rather limited technical report series, I'm making PDFs available for download here.

Follow this link for a more complete list of my publications.


This book is a good introduction to the theory of graphical belief function models and provides a good solid grounding in the theoretical basis of both BELIEF and Graphical-Belief.

Almond, R.G. [1995a]
Graphical Belief Modeling Chapman and Hall (Click Here For Ordering Information. (ISBN 0-412-06661-0). Revised version of Fusion and Propagation in Graphical Belief Models: An Implementation and an Example. Ph.D. dissertation and Harvard University, Department of Statistics Technical Report S-130.

Overviews and Examples

These two papers were written as examples of Graphical-Belief in action. Although they are now somewhat dated, they provide a good overview of the kinds of problems Graphical-Belief can address and review some of the basic properties of the system.

Almond, R.G. [1992]
``An Extended Example for Testing GRAPHICAL-BELIEF.'' StatSci Research Report 6. (PDF) This example goes over the simple reliability example from Martz and Waller [1990].
Almond, R.G. and Madigan, D. [1993]
``Using GRAPHICAL-BELIEF to Predict Risk or Coronary Artery Disease.'' StatSci Research Report 19. (PDF) This example goes over a simple medical risk example fit to data from Detrano et al. [1989].

Published Papers

This section lists various papers available in published journals and conference proceedings related to the Graphical-Belief project.

Madigan, D., K. Mosurski and R.G. Almond [1997]
``Graphical Explanation in Belief Networks.'' Journal of Computational and Graphical Statistics, 6, 160-181.
Madigan, D. and R.G. Almond [1995]
``Test Selection Strategies for Belief Networks'' StatSci Research Report 20. In D. Fisher and H.J. Lenz (eds) Learning from Data: AI and Statistics V Springer-Verlag, pp 89-98. Describes the use of weight of evidence to select tests.
Almond, R. G., Bradshaw, J.M., Madigan, D. [1994]
``Reuse and Sharing of Graphical Belief Network Components.'' in P. Cheeseman and W. Oldford (eds.) Selecting Models from Data: Artificial Intelligence and Statistics IV, Springer-Verlag, 113--122. Describes the basic knowledge structures used in graphical belief models.
Madigan, D., Raftery, A. E., York, J. C., Bradshaw, J. M. and Almond, R. G. [1994]
``Strategies for Graphical Model Selection.'' in P. Cheeseman and W. Oldford (eds.) Selecting Models from Data: Artificial Intelligence and Statistics IV, Springer-Verlag, 91--100. Compares two techniques for selecting models from data accounting for model uncertainty.
Madigan, D., York, J.C. Bradshaw, J.M. and Almond, R.G. [1994]
``Bayesian Graphical Models for Predicting Errors in Databases.'' in P. Cheeseman and W. Oldford (eds.) Selecting Models from Data: Artificial Intelligence and Statistics IV, Springer-Verlag, 123--132. Describes an application of graphical models.
Almond, R.G. [1993]
``Lack of Information Based Control in Expert Systems.'' In Hand, D.J (ed). Artificial Intelligence Frontiers in Statistics: AI and Statistics III, Chapman and Hall, pp 82--89. Describes a technique for tracing information through a graphical model.
Bradshaw, J.M., Chapman, C.R., Sullivan, K.M., Almond, R.G., Madigan, D., Zarley, D., Gavrin, J., Nims, J., and Bush, N. [1992].
``KS-3000: an application of DDUCKS to bone-marrow transplant patient support.'' In Proceedings of the Sixth Annual Florida AI Research Symposium (FLAIRS '93), Ft. Lauderdale, FL, 78--83. Describes an application in which a graphical model engine is embedded in a more complex knowledge based performance support system. (Word Document)
Almond, Russell G. [1991]
``Building Blocks for Graphical Belief Models.'' Journal Of Applied Statistics, 18, 63--76. Describes some simple belief function models.
Almond, R.G. [1988]
``Fusion and Propagation in Graphical Belief Models.'' Computing Science and Statistics: Proceedings of the 20th Symposium on the Interface. Wegman, Edward J., Gantz, Donald T. and Miller, John J. (ed.). American Statistical Association, Alexandria, Virginia. pp 365--370. (Click Here to Download an extended version.) Describes a simple example of a graphical belief function.

Technical Reports and Prepublications

The following reports and conference presentations describe important developments in the graphical belief project.

Almond, R.G. [1995]
``Hypergraph Grammars for Knowledge Based Model Construction.'' StatSci Research Report 23. Presented at the 5th International Workshop on AI and Statistics, Ft. Lauderdale Florida. (Click Here to Download) This report describes the object-oriented model construction features.
Almond, R.G. [1995]
``Capturing Reliability Knowledge in GRAPHICAL-BELIEF.'' StatSci Research Report 31. Presented in special NASA collection. (Click Here to Download) Describes how the object-oriented model construction would work in a distributed engineering environment.
Almond, R.G. [1994]
``Brushing Histories to Compare Models'' StatSci Research Report 17. (Under Revision for Publication). (Click Here to Download) Shows the mechanism used to compare changes in the model and study sensitivity.
Almond, R.G. [1992]
``Models for Incomplete Failure Data.'' StatSci Research Report 9. (Click Here to Download) Describes the difference between belief function and probability models.

Old Technical Reports

These are older technical reports which have largely been superceded by more recent material. They occasionally contain material of historical interest or which is not available elsewhere.

Almond, R.G. [1992]
``Reduced Parameter Representation of Model Components'', StatSci Research Report 1. (Click Here to Download) This describes a mechanism for finding structure within the rules which define the relationships among a few variables.
Almond, R.G. [1991]
``Fiducial Inference and Belief Functions.'' Technical Report 206, University of Washington, Department of Statistics. (Click Here to Download) Describes the origins of belief functions from Fisher's ideas about fiducial inference.
Almond, R.G. and Kong, C-T. A. [1991]
``Some Heuristics for Building an Optimal Tree of Cliques from a Graph or Hypergraph.'' University of Chicago, Department of Statistics, Research Report 329. (Click Here to Download) This describes the procedure for building the junction tree. Most of the key results are available in the book.

Explore an example of Graphical-Belief in action.

Get more information about obtaining Graphical-Belief .

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

Russell Almond, <lastname> (at)
Last modified: Jan 9 2006