Random Variables: Expectation and Independence

Related lectures (30)

Page 3 of 3

Covers the fundamentals of probability theory, including corollaries, conditional probability, total probability theorem, and random variables.

Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.

Offers a review of key probability concepts, including probability spaces, events, and random variables.

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

Explores discrete and continuous random variables, independence, and probability functions.

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Explores different modes of convergence for random variables.

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Explores conditional density, expectations, and independence of random variables with practical examples.