Skip to main content
SpringerLink
Log in

Graphical Models as Languages for Computer Assisted Diagnosis and Decision Making

  • Conference paper
  • First Online:
Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2143))

  • 737 Accesses

Abstract

Over the last decade, graphical models for computer assisted diagnosis and decision making have become increasingly popular. Graphical models were originally introduced as ways of decomposing distributions over a large set of variables. However, the main reason for their popularity is that graphs are easy for humans to survey, and most often humans take part in the construction, test, and use of systems for diagnosis and decision making. In other words, at various points in the life cycle of a system, the model is interpreted by a human or communicated between humans. As opposed to machine learning, we shall call this activity human interacted modeling. In this paper we look at graphical models from this point of view. We introduce various kinds of graphical models, and the comprehensibility of their syntax and semantics is in focus.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. C. Bielza and Prakash P. Shenoy. A comparison of graphical techniques for asymmetric decision problems. Management Science, 45(11):1552–1569, 1999.

    MATH  Google Scholar 

  2. Remco R. Bouckaert and Milan Studený. Chain graphs: Semantics and expressiveness. In Christine Froidevaux and Jürg Kohlas, editors, Lecture Notes in Computer Science, volume 946, pages 69–76. Springer, 1995. Proceedings of ECSQARU 1995, Fribourg, Switzerland.

    Google Scholar 

  3. Z. Covaliu and R. M. Oliver. Representation and solution of decision problems using sequential decision diagrams. Management Science, 41(12):1860–1881, 1995.

    MATH  Google Scholar 

  4. Morten Frydenberg. The chain graph Markov property. Scandinavian Journal of Statistics, 17:333–353, 1990.

    MATH  MathSciNet  Google Scholar 

  5. Frank Jensen, Finn V. Jensen, and Søren L. Dittmer. From influence diagrams to junction trees. In Ramon Lopez de Mantaras and David Poole, editors, Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, pages 367–373, San Francisco, 1994. Morgan Kaufmann, San Francisco, CA.

    Google Scholar 

  6. Steffen L. Lauritzen and Finn V. Jensen. Local computation with valuations from a commutative semigroup. Annals of Mathematics and Artificial Intelligence, 21:51–69, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  7. Steffen L. Lauritzen and T. S. Richardson. Chain graph models and their causal interpretation. Technical Report R-01-2003, Department of Mathematical Sciences, Aalborg University, 2001.

    Google Scholar 

  8. Steffen L. Lauritzen and Nanny Wermuth. Graphical models for associations between variables, some of which are qualitative and some quantitative. The Annals of Statistics, 17(1):31–57, 1989.

    Article  MATH  MathSciNet  Google Scholar 

  9. Thomas D. Nielsen. Decomposition of influence diagrams. 2001. In these proceedings.

    Google Scholar 

  10. Thomas D. Nielsen and Finn V. Jensen. Well-defined decision scenarios. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 502–511. Morgan Kaufmann Publishers, San Francisco, CA., 1999.

    Google Scholar 

  11. Thomas D. Nielsen and Finn V. Jensen. Representing and solving asymmetric Bayesian decision problems. In Craig Boutilier and Moises Goldszmidt, editors, Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pages 416–425. Morgan Kaufmann Publishers, San Francisco, CA., 2000.

    Google Scholar 

  12. Dennis Nilsson and Steffen L. Lauritzen. Evaluating influence diagrams using LIM-IDs. In Craig Boutilier and Moises Goldszmidt, editors, Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pages 436–445. Morgan Kaufmann Publishers, San Francisco, CA., 2000.

    Google Scholar 

  13. Runping Qi, Lianwen Zhang, and David Poole. Solving asymmetric decision problems with influence diagrams. In R. Lopez de Mantaras and D. Poole, editors, Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pages 491–497. Morgan Kaufmann Publishers, San Francisco, CA., 1994.

    Google Scholar 

  14. Ross D. Shachter. Evaluating influence diagrams. Operations Research, 34(6):871–882, 1986.

    MathSciNet  Google Scholar 

  15. Ross D. Shachter. Bayes-Ball: The rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams). In Gregory F. Cooper and Serafín Moral, editors, Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pages 480–487. Morgan Kaufmann Publishers, San Francisco, CA., 1998.

    Google Scholar 

  16. Glenn R. Shafer and Prakash Shenoy. Probability propagation. Annals of Mathematics and Artificial Intelligence, 2:327–352, 1990.

    Article  MATH  MathSciNet  Google Scholar 

  17. Prakash P. Shenoy. Valuation-based systems for Bayesian decision analysis. Operations Research, 40(3):463–484, 1992.

    Article  MATH  MathSciNet  Google Scholar 

  18. Prakash P. Shenoy. Valuation network representation and solution of asymmetric decision problems. European Journal of Operations Research, 121(3):579–608, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  19. M. Sochorová and Jirí Vomlel. Troubleshooting: NP-hardness and solutions methods. In J. Vejnarová, editor, Proceedings of Fifth Workshop on Uncertainty Processing. Jindrichůuv Hradec, Czech Republic, 2000.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Aalborg University, Fredrik Bajers Vej 7E, DK-9220, Aalborg, Denmark

    Finn Verner Jensen

Authors
  1. Finn Verner Jensen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Université Paul Sabatier, IRIT-CNRS, 118 route de Narbonne, 31062, Toulouse Cedex 4, France

    Salem Benferhat  & Philippe Besnard  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jensen, F.V. (2001). Graphical Models as Languages for Computer Assisted Diagnosis and Decision Making. In: Benferhat, S., Besnard, P. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2001. Lecture Notes in Computer Science(), vol 2143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44652-4_1

Download citation

  • DOI: https://doi.org/10.1007/3-540-44652-4_1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42464-2

  • Online ISBN: 978-3-540-44652-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation