Executive Summary: Bayes Nets Andrew W. Moore Carnegie Mellon University, School of Computer Science Note to other teachers.

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Executive Summary: Bayes Nets Andrew W. Moore Carnegie Mellon University, School of Computer Science Note to other teachers and users of these slides. Andrew would be delighted if you found this source material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. PowerPoint originals are available. If you make use of a significant portion of these slides in your own lecture, please include this message, or the following link to the source repository of Andrew’s tutorials: Comments and corrections gratefully received.

Copyright © 2002, Andrew W. MooreShort Bayes Nets: Slide 2 Ice-Cream and Sharks Spreadsheet Recent Dow- Jones Change Number of Ice- Creams sold today Number of Shark Attacks today UP35004 STEADY410 UP23005 DOWN34004 UP180 STEADY1050 STEADY UP700

Copyright © 2002, Andrew W. MooreShort Bayes Nets: Slide 3 A simple Bayes Net Common Cause Shark Attacks Ice-Cream Sales

Copyright © 2002, Andrew W. MooreShort Bayes Nets: Slide 4 A bigger Bayes Net Number of People on Beach Shark Attacks Ice-Cream Sales Fish Migration Info Is it a Weekend? Weather These arrows can be interpreted… Causally (Then it’s called an influence diagram) Probabilistically (if I know the value of my parent nodes, then knowing other nodes above me would provide no extra information: Conditional Independence

Copyright © 2002, Andrew W. MooreShort Bayes Nets: Slide 5 The Guts of a Bayes Net Number of People on Beach Shark Attacks Ice-Cream Sales Fish Migration Info Is it a Weekend? Weather If Sunny and Weekend then Prob ( Beach Crowded ) = 90% If Sunny and Not Weekend then Prob ( Beach Crowded ) = 40% If Not Sunny and Weekend then Prob ( Beach Crowded ) = 20% If Not Sunny and Not Weekend then Prob ( Beach Crowded ) = 0%

Copyright © 2002, Andrew W. MooreShort Bayes Nets: Slide 6 Real-sized Bayes Nets How do you build them? From Experts and/or from Data! How do you use them? Predict values that are expensive or impossible to measure. Decide which possible problems to investigate first.

Copyright © 2002, Andrew W. MooreShort Bayes Nets: Slide 7 Building Bayes Nets Bayes nets are sometimes built manually, consulting domain experts for structure and probabilities. More often the structure is supplied by experts, but the probabilities learned from data. And in some cases the structure, as well as the probabilities, are learned from data.

Copyright © 2002, Andrew W. MooreShort Bayes Nets: Slide 8 Example: Pathfinder Pathfinder system. (Heckerman, Probabilistic Similarity Networks, MIT Press, Cambridge MA). Diagnostic system for lymph-node diseases. 60 diseases and 100 symptoms and test-results. 14,000 probabilities. Expert consulted to make net. 8 hours to determine variables. 35 hours for net topology. 40 hours for probability table values. Apparently, the experts found it quite easy to invent the causal links and probabilities. Pathfinder is now outperforming the world experts in diagnosis. Being extended to several dozen other medical domains.

Copyright © 2002, Andrew W. MooreShort Bayes Nets: Slide 9 Other Bayes Net Examples: Further Medical Examples (Peter Spirtes, Richard Scheines, CMU) Manufacturing System diagnosis (Wray Buntine, NASA Ames) Computer Systems diagnosis (Microsoft) Network Systems diagnosis Helpdesk (Support) troubleshooting (Heckerman, Microsoft) Information retrieval (Tom Mitchell, CMU) Customer Modeling Student Retention (Clarke Glymour, CMU Nomad Robot (Fabio Cozman, CMU)