Covers the essentials of probability, algebras, and conditional probability, including the Borel o-algebra and Poisson processes.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Introduces key concepts in probability and statistics, illustrating their application through various examples and emphasizing the importance of mathematical language in understanding the universe.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
