Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.
Covers the essentials of probability, algebras, and conditional probability, including the Borel o-algebra and Poisson processes.
