Bayesian inference

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Across
  1. 2. Table of conditional probabilities in Bayesian networks.
  2. 7. Graph structure of a Bayesian Network (Directed Acyclic Graph).
  3. 8. Formula for computing posterior probability.
  4. 9. The probability distribution before observing data.
  5. 11. Step that ensures probabilities sum to one.
  6. 12. Combined probability distribution over multiple variables.
  7. 13. Approach that updates beliefs based on evidence.
  8. 15. Process of breaking joint probability into smaller parts.
  9. 16. A probability measure representing confidence in a variable.
  10. 18. Probability of the evidence given a hypothesis.
  11. 20. When one variable does not influence another.
Down
  1. 1. Act of revising probabilities when new data arrives.
  2. 3. Updated probability after considering new evidence.
  3. 4. The variable for which probability is calculated.
  4. 5. Probability helps in modeling this unknown factor.
  5. 6. Summing out a variable from a joint distribution.
  6. 10. A measurable quantity represented as a node in BN.
  7. 14. Type of probability represented as P(A
  8. 17. Observed data used to update beliefs.
  9. 19. The process of deriving conclusions from data and models.