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Belief networks

Explore conditional independence, syntax, semantics, and exact & approximate inference in belief networks. Learn constructing algorithms and examples of car diagnosis and insurance. CS 561 Session 29.

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Belief networks

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  1. Belief networks • Conditional independence • Syntax and semantics • Exact inference • Approximate inference CS 561, Session 29

  2. Independence CS 561, Session 29

  3. Conditional independence CS 561, Session 29

  4. Conditional independence CS 561, Session 29

  5. Conditional independence CS 561, Session 29

  6. Belief networks CS 561, Session 29

  7. Example CS 561, Session 29

  8. Semantics CS 561, Session 29

  9. Semantics CS 561, Session 29

  10. Markov blanket CS 561, Session 29

  11. Constructing belief networks CS 561, Session 29

  12. Example CS 561, Session 29

  13. CS 561, Session 29

  14. CS 561, Session 29

  15. CS 561, Session 29

  16. CS 561, Session 29

  17. Example: car diagnosis CS 561, Session 29

  18. Example: car insurance CS 561, Session 29

  19. Compact conditional distributions CS 561, Session 29

  20. Compact conditional distributions CS 561, Session 29

  21. Hybrid (discrete+continuous) networks CS 561, Session 29

  22. Continuous child variables CS 561, Session 29

  23. Continuous child variables CS 561, Session 29

  24. Discrete variable w/ continuous parents CS 561, Session 29

  25. Discrete variable CS 561, Session 29

  26. Inference in belief networks • Exact inference by enumeration • Exact inference by variable elimination • Approximate inference by stochastic simulation • Approximate inference by Markov chain Monte Carlo (MCMC) CS 561, Session 29

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