3 – SL NB 02 Known And Inferred V1 V2

In the last video, we saw an example of Bayes theorem. But here’s the main idea fit and it’s a very powerful theorem. What it does is it switches from what we know to what we infer. What we know in this case is the probability that Alex wears red and the probability that Brenda wears red. And what we infer is the opposite, is the probability that someone wearing red is Alex or that someone wearing red is Brenda. In other words, initially we know the probability of an event A. And to give us more information, we introduce a new event R which is related to A. We know the probability of R given A. What Bayes’ theorem does is from these two, it infers the probability of A given R. This is the new probability of A once we know that the event R has occurred