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Concept

Chain rule (probability)

Related lectures (25)

Covers the chain rule in graphical models and introduces variable elimination.

Explores the Chow-Liu Algorithm for structure learning and optimizing distributions through spanning trees and K-L divergence.

Explores sampling in maximum likelihood estimation and its implications on the joint probability and likelihood contribution.

Covers the theory and practical applications of linear regression.

Explores the concept of independence in probability theory, covering events, random variables, and sigma-fields.

Explores discrete random variables, joint probabilities, and medical testing quality using Bayes' theorem.

Explores goodness-of-fit tests, X² tests, and independence tests in statistics, with practical examples and applications.

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Covers the simplest model for overlapping events and their probabilities.

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Covers phase space, ensembles of particles, probability distributions, and probability density functions.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Covers discrete and continuous probability distributions, including joint and conditional probabilities.

Discusses conditional entropy and its role in information theory and data compression.

Explores entropy in information theory and its role in data processing and probability distributions.

Covers information measures, tail bounds, subgaussions, subpossion, independence proof, and conditional expectation.

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

Introduces elementary probability theory, set theory operations, and probability calculations with practical examples.

Explains prediction decomposition in probability theory with examples and calculations.

Covers fundamental concepts of probability theory and measure theory, including joint probabilities, random variables, and the central limit theorem.