Lecture
This lecture introduces the concept of conditional probability, focusing on how the occurrence of one event affects the probabilities of others. The instructor begins by explaining the foundational idea of updating probabilities based on new information. The definition of conditional probability is presented, emphasizing the relationship between events A and B. The instructor illustrates this with examples, demonstrating how knowing that event B has occurred changes the probability of event A. The lecture further explores the mathematical formulation of conditional probability, including the multiplication rule and the law of total probability. The instructor provides practical examples, such as rolling dice, to clarify these concepts. Additionally, the lecture touches on Bayes' theorem, which allows for the calculation of probabilities in reverse scenarios. The importance of understanding conditional probability in real-world applications, such as medical testing and decision-making, is highlighted. The instructor concludes by discussing the implications of false positives in testing scenarios, emphasizing the need for careful interpretation of results.