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Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.
Covers random vectors, joint probability density, independent random variables, functions of two random variables, and Gaussian random variables.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Explores independence and covariance between random variables, discussing their implications and calculation methods.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers the Central Limit Theorem and its application to random variables, proving convergence to a normal distribution.
