Stochastic Models for Communications

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Related lectures (30)

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Probability and Statistics: Fundamentals

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

Central Limit Theorem: Properties and Applications

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

Probability Theory: Midterm Solutions

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Random Vectors: Stochastic Models for Communications

Covers random vectors, joint probability, and Gaussian random variables in communication models.

Statistical Inference: Random Variables

Covers random variables, probability functions, expectations, variances, and joint distributions.

Probability Distributions in Environmental Studies

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Independence and Covariance

Explores independence and covariance between random variables, discussing their implications and calculation methods.

Advanced Probabilities: Random Variables & Expected Values

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

Conditional Density and Expectation

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

Probability Theory: Random Variables and Covariance

Covers random variables, covariance, and joint probability distributions.