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Building Bayesian Networks

Building Bayesian Networks. COMPSCI 276, Fall 2009 Set 3: Rina Dechter. (Reading: Darwiche chapter 5). Queries : Different queries may be relevant for different scenarios. For other tools see class page.

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Building Bayesian Networks

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  1. Building Bayesian Networks COMPSCI 276, Fall 2009 Set 3: RinaDechter (Reading: Darwichechapter 5)

  2. Queries: Different queries may be relevant for different scenarios

  3. For other tools see class page

  4. Other type of evidence: We may want to know the probability that the patient has either a positive X-ray or dyspnoea, X =yes or D=yes.

  5. C= lung cancer

  6. Building email management network • Step 1: define the variables: email characteristics • Title, • (values: any sequence of words.) • sender-id, • (values: # of id names) • Recipients, #-of-recipients • (Values, a sequence of id-names) • topic, • (values: a distribution over bag of words, or a set of key words) • length, • (values: natural numbers) • time-sent : (time-of-week, time-of-day), • (values: days of the week, time (discredized) • time-read, (values: as above) • current-time, (value: as above) • max-reponse-time (value: as above) Evidence variables query

  7. Variables? Arcs? Try it.

  8. A naive Bayes structure has the following edges C -> A1, . . . , C -> Am, where C is called the class variable and A1; : : : ;Am are called the attributes. What about?

  9. Learn the model from data

  10. Learning the model

  11. Try it: Variables and values? Structure? CPTs?

  12. Try it: Variables? Values? Structure?

  13. Variables? Values? Structure?

  14. Try it: Variables, values, structure?

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