Covers the normal distribution, inferential statistics, probability, and the binomial distribution in the context of the 'Dishonest Gambler Problem'.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers an example of Bayesian parameter estimation and the trade-off between bias and variance in supervised learning.
Explores copulas for measuring dependence strength in distributions and transforming variables to unit Fréchet margins.
Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.
