Conditional Probability & Bayes' Theorem (A Class Session, Oct. 16, 2025) скачать в хорошем качестве

Conditional Probability & Bayes' Theorem (A Class Session, Oct. 16, 2025)

Conditional Probability & Bayes' Theorem Conditional Probability Conditional probability is a measure of the probability of an event occurring, given that another event has already occurred. For instance, the conditional probability of an event A is the probability that the event will occur given the knowledge that an event B has already occurred. Bayes’ Theorem The goal here is to calculate the probability P(A/B) from information about P(B/A). Put simply, Bayes’ theorem help us find the chance or the probability that some event (or an event) will occur, given what we already know. Theorem Highlights: Suppose two events have prior probabilities P(A1) and P(A2). Then A1 and A2 are called prior probabilities because they are determined without any other information. The goal here is to find the posterior probabilities P(Ai/Bi). Note that the prior and posterior probabilities refers to the probabilities before and after observing an event Bi.