Exact inference in BN

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Across
  1. 1. Observed variable values used during inference.
  2. 4. Situation where one event does not affect another.
  3. 6. Table storing conditional probabilities in BN (Abbreviation).
  4. 8. Method used to compute joint distributions in BN (single word form).
  5. 11. Combined probability distribution over multiple variables.
  6. 12. The variable for which we want a probability answer.
  7. 15. The variable for which the probability is sought (single word form).
  8. 16. Directed connection between nodes in BN.
  9. 17. Posterior probability distribution for a variable.
Down
  1. 2. Exact inference becomes difficult due to this in large networks.
  2. 3. A function mapping variable assignments to probabilities.
  3. 4. The process of computing probabilities in a Bayesian network.
  4. 5. Probability given that another event has occurred.
  5. 7. Probability of a single variable independent of others.
  6. 9. Represents a variable in BN.
  7. 10. Type of network that represents probabilistic relationships.
  8. 13. Numerical value expressing uncertainty.
  9. 14. Structure of Bayesian network (Abbreviation for Directed Acyclic Graph).
  10. 18. Algorithm that removes variables to compute probabilities.