Computational Models for Probabilistic Reasoning

• What we want

  • a “probabilistic knowledge base” where domain knowledge is represented by propositions, unconditional, and conditional probabilities
  • an inference engine that will computeProb(formula | “all evidence collected so far”)

• Problems

  • elicitation: what parameters do we need to ensure a complete and consistent knowledge base?
  • computation: how do we compute the probabilities efficiently?

• Answer (to both problems)

  • a representation that makes structure (dependencies and independencies) explicit

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