Random Vectors & Distribution Functions

Related lectures (30)

Page 3 of 3

Covers copulas, Sklar's Theorem, types of copulas, and simulation of copulas for risk management.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

Explores moment-generating functions, linear combinations, and normality of random variables.

Explores independence and covariance between random variables, discussing their implications and calculation methods.

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

Explores covariance, correlation, and mutual information in quantifying statistical dependence between random variables.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Covers random vectors, stochastic models, functions, matrices, and expectations in communication systems.