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 random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
