## Notes

• 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