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Applying Dempster -Shafer Theory on a Simple Graphical Network 7 December 2010

Applying Dempster -Shafer Theory on a Simple Graphical Network 7 December 2010. Matthias Chan Jess Stigile Department of Electrical and Systems Engineering Washington University in St. Louis Advisors : Dr. Sung-Hyun Son, MIT Lincoln Laboratory Dr. Keh -Ping Dunn, MIT Lincoln Laboratory

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Applying Dempster -Shafer Theory on a Simple Graphical Network 7 December 2010

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  1. Applying Dempster-Shafer Theory on a Simple Graphical Network7 December 2010 Matthias Chan Jess Stigile Department of Electrical and Systems Engineering Washington University in St. Louis Advisors: Dr. Sung-Hyun Son, MIT Lincoln Laboratory Dr. Keh-Ping Dunn, MIT Lincoln Laboratory Dr. AryeNehorai, Washington University in St. Louis

  2. Outline

  3. Introduction

  4. Pattern Classification – Formulation

  5. Pattern Classification – Methods

  6. Pattern Classification – Difficulties

  7. Graphical Model Basics

  8. Bayesian Networks Cloudy Sprinkler Rain WetGrass

  9. Bayesian Networks – An Example Cloudy Sprinkler Rain WetGrass Murphy, Kevin. “An Introduction to Graphical Models.” Pages 2-3.

  10. Inference

  11. Dempster Shafer Theory

  12. Dempster-Shafer Basics Portion of Earth that we KNOW is water Portion of Earth that COULD be water if all of the unknown area is water

  13. Problem Statement

  14. Problem Introduction

  15. Graphical Model

  16. Bayes’ Rule Bayes’ Rule Only 1 generation of dependence

  17. Dempster-Shafer Example

  18. Dempster-Shafer Transitions

  19. Dempster-Shafer Equations • Region where X13MUST be A • Region where X13COULD be A

  20. Conclusions

  21. Future Work

  22. Acknowledgments

  23. References

  24. Questions?

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