Being Bayesian

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  • Describing knowledge of a system probabilistically, having an appropriate prior probability, know how to weigh new evidence, and following Bayes’s rule to compute the revised distribution.
  • How Yoshi sends signals about her food preferences.
  • Prob x given y
  • Posterior probability and prior probability
  • Distribution after and distribution before
  • Prior times likelihood is posterior
  • Converges quickly to new posterior