Mutual Information: Understanding Random Variables

Related lectures (31)

Page 3 of 4

Introduces random variables, probability distributions, and expected values through practical examples.

Explores nonparametric relationships in GLMs and the importance of understanding variability through probability and models.

Explores the chain rule for entropy, decomposing uncertainty in random variables and illustrating its application with examples.

Introduces information theory basics, including entropy, independence, and binary entropy function.

Covers the basics of information theory, focusing on Shannon's setting and channel transmission.

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

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

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.

Explores channel capacity and convex functions in information theory, emphasizing the importance of convexity.