Expectation: Basic Properties

Related lectures (31)

Page 3 of 4

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

Explores conditional expectation properties, including measurability, linearity, and independence of random variables.

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

Covers conditional expectation, convergence of random variables, and the strong law of large numbers.

Introduces random variables, probability distributions, and expected values through practical examples.

Introduces the basics of conditional expectation, covering definitions, properties, and examples in the context of random variables.

Explores the transformation of random variables, joint moments, covariance, and variance.

Covers the properties of expectation and examples of calculating expectations for random variables.

Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.

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