Random Vectors and Stochastic Models for Communications

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Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

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

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

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

Covers the multivariate normal distribution, properties, and sampling methods.

Covers probability, statistics, independence, covariance, correlation, and random variables.

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

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

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

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