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How Are Bayesian Networks Constructed?

  • Subjective construction: Identification of (direct) causal structure
    • People are quite good at identifying direct causes from a given set of variables & whether the set contains all relevant direct causes
    • Markovian assumption: Each variable becomes independent of its non-effects once its direct causes are known
    • E.g., S ‹— F —› A ‹— T, path S—›A is blocked once we know F—›A
    • HMM (Hidden Markov Model): often used to model dynamic systems whose states are not observable, yet their outputs are
  • Synthesis from other specifications
    • E.g., from a formal system design: block diagrams & info flow
  • Learning from data
    • E.g., from medical records or student admission record
    • Learn parameters give its structure or learn both structure and parms
    • Maximum likelihood principle: favors Bayesian networks that maximize the probability of observing the given data set

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