Introduces the Metropolis-Hastings algorithm for efficient simulation of random variables with given probabilities.
Covers the Central Limit Theorem and its application to random variables, proving convergence to a normal distribution.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.
