Expectation and Variance

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Related lectures (29)

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Law of Large Numbers: Strong Convergence

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

Probability and Statistics: Fundamental Theorems

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Conditional Density and Expectation

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

Probability and Statistics

Explores joint random variables, conditional density, and independence in probability and statistics.

Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Continuous Random Variables

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Variance and Covariance: Properties and Examples

Explores variance, covariance, and practical applications in statistics and probability.

Probability and Statistics

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Probability and Statistics

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

Probability and Statistics: Fundamentals

Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.