Random Variables and Probability Densities

Related lectures (30)

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

Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.

Covers random vectors, joint probability density, independent random variables, functions of two random variables, and Gaussian random variables.

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

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

Covers the normal distribution, including its properties and calculations.

Covers conditional probability distributions and introduces the concept of conditional expected value.

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

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

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