Uploaded October 2021 | Updated September 2026, 3 weeks ago
This is Prof. Matt McCormick's second lecture on Bayes' Theorem. We consider base rates, likelihoods, and some applications of Bayes' Theorem. What does changing the base rate or the prior probability do to the output of the equation? More importantly, how do we update a belief we have when we make an observation that may count for or against that belief. Bayes' theorem gives us a formal model with answers that can help us retrain our intuitions.
This is Prof. Matt McCormick's second lecture on Bayes' Theorem. We consider base rates, likelihoods, and some applications of Bayes' Theorem. What does changing the base rate or the prior probability do to the output of the equation? More importantly, how do we update a belief we have when we make an observation that may count for or against that belief. Bayes' theorem gives us a formal model with answers that can help us retrain our intuitions.
