Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores combinatorial mathematics, covering permutations, combinations, and binomial coefficients, along with probability and statistics concepts.
Introduces key concepts in probability and statistics, illustrating their application through various examples and emphasizing the importance of mathematical language in understanding the universe.
Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Covers fundamental concepts in probability and statistics, including the law of total probability, Bayes' theorem, and independence of events.
Covers fundamental concepts in probability and statistics, including distributions, properties, and expectations of random variables.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Covers properties and transformations of discrete random variables, focusing on PMF and expectation.
