Random Vectors and Gaussian Random Vectors

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Explores various continuous probability distributions and expectation of random variables.

Explores continuous random variables, cdf, pdf, convolution, and sum of dice.

Explores quantiles, sampling, and histogram density for understanding distributions and constructing confidence intervals.

Defines the expectation for random variables, emphasizing the importance of absolute values.

Covers the modes of convergence of random variables and the Central Limit Theorem, discussing implications and approximations.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Explores random variables, expectation, and independence in probability theory.

Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.