Multivariable Control

Related lectures (31)

Page 1 of 4

Explores Gaussian processes, linear systems, transformations, and noise properties in multivariable control applications.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Covers the multivariate normal distribution, properties, and sampling methods.

Explores joint random variables, conditional density, and independence in probability and statistics.

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Covers conditional density, independence of random variables, expectation, and variance calculation.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

Explores conditional density, expectations, and independence of random variables with practical examples.

Explores statistical models, families of distributions, transformations, and their applications in probability theory.