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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