Probability II builds on Probability I and introduces more advanced concepts and techniques in probability theory. The course covers joint, marginal, and conditional distributions of multiple random variables, independence, covariance, correlation, and conditional expectation. It also includes moment-generating functions, the law of total probability, Bayes’ theorem in multivariate settings, and theoretical results such as the Law of Large Numbers and the Central Limit Theorem. The course emphasizes mathematical modeling and analytical problem-solving and prepares students for advanced topics in mathematical statistics and stochastic processes.