Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
