Copulas and Margins

Related lectures (30)

Page 3 of 3

Explores joint distributions, marginal laws, covariance, correlation, and variance properties.

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

Covers the transformations of joint continuous densities and their implications on probability distributions.

Covers the transformations of continuous random variables and the determinant of Jacobians.

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Covers random variables, probability functions, expectations, variances, and joint distributions.

Investigates how birth month influences athlete success, analyzing Japanese athletes' dataset to explore trends in birthdates and occupations.

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

Introduces multivariate statistics, focusing on uncovering associations between components in data in vector form.

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.