Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.
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 fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.
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.
Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.
Covers the Central Limit Theorem and its application to random variables, proving convergence to a normal distribution.
Covers random vectors, joint probability density, independent random variables, functions of two random variables, and Gaussian random variables.
