Lectures related to Chain rule (probability)

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.

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

Covers the theory and practical applications of linear regression.

Explains prediction decomposition in probability theory with examples and calculations.

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.

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

Explores joint probability, marginal laws, conditional laws, and potential paradoxes in probability theory.

Covers the basics of probability theory, random variables, joint probability, and inference.

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

Covers random vectors, joint probability, and Gaussian random variables in communication models.

Explores statistical independence, correlation, Gauss functions, likelihood estimation, and Gaussian Mixture Models for clustering.

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.