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10/24

10/24. Exam on 10/26 (Lei Tang and Will Cushing to proctor). Exact Inference Complexity NP-hard (actually #P-Complete; since we “count” models) Polynomial for “Singly connected” networks (one path between each pair of nodes) Algorithms Enumeration Variable elimination

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10/24

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  1. 10/24 Exam on 10/26 (Lei Tang and Will Cushing to proctor)

  2. Exact Inference Complexity NP-hard (actually #P-Complete; since we “count” models) Polynomial for “Singly connected” networks (one path between each pair of nodes) Algorithms Enumeration Variable elimination Avoids the redundant computations of Enumeration [Many others such as “message passing” algorithms, Constraint-propagation based algorithms etc.] Approximate Inference Complexity NP-Hard for both absolute and relative approximation Algorithms Based on Stochastic Simulation Sampling from empty networks Rejection sampling Likelihood weighting MCMC [And many more] Overview of BN Inference Algorithms TONS OF APPROACHES

  3. Examples of singly connected networks include Markov Chains and Hidden Markov Models

  4. fA(a,b,e)*fj(a)*fM(a)+ fA(~a,b,e)*fj(~a)*fM(~a)+

  5. Notice that sampling methods could in general be used even when we don’t know the bayes net (and are just observing the world)! We should strive to make the sampling more efficient given that we know the bayes net

  6. That is, the rejection sampling method doesn’t really use the bayes network that much…

  7. Notice that to attach the likelihood to the evidence, weare using the CPTs in the bayes net. (Model-free empirical observation, in contrast, either gives you a sample or not; we can’t get fractional samples)

  8. MCMC not covered on 10/24

  9. Note that the other parents of zj are part of the markov blanket

  10. Case Study: Pathfinder System • Domain: Lymph node diseases • Deals with 60 diseases and 100 disease findings • Versions: • Pathfinder I: A rule-based system with logical reasoning • Pathfinder II: Tried a variety of approaches for uncertainity • Simple bayes reasoning outperformed • Pathfinder III: Simple bayes reasoning, but reassessed probabilities • Parthfinder IV: Bayesian network was used to handle a variety of conditional dependencies. • Deciding vocabulary: 8 hours • Devising the topology of the network: 35 hours • Assessing the (14,000) probabilities: 40 hours • Physician experts liked assessing causal probabilites • Evaluation: 53 “referral” cases • Pathfinder III: 7.9/10 • Pathfinder IV: 8.9/10 [Saves one additional life in every 1000 cases!] • A more recent comparison shows that Pathfinder now outperforms experts who helped design it!!

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