Random Variables: Basics

Related lectures (29)

Page 3 of 3

Explores different modes of convergence for random variables.

Explores joint distributions, marginal laws, covariance, correlation, and variance properties.

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Covers fundamental concepts in probability and statistics, including distributions, properties, and expectations of random variables.

Discusses vectors of random variables and empirical distributions, including their properties and significance in statistics.

Covers the normal distribution, including its properties and calculations.

Covers random vectors, joint probability, and conditional probability in communication stochastic models.

Covers conditional density, independence of random variables, expectation, and variance calculation.