Copulas and Margins

Related lectures (30)

Page 2 of 3

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Covers random vectors, joint distribution, conditional density functions, independence, covariance, correlation, and conditional expectation.

Covers elliptical distributions, including properties, applications, and risk management implications.

Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.

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

Covers the properties and applications of copulas, including examples and coefficients of tail dependence.

Covers copulas, Sklar's Theorem, meta distributions, and various dependence measures like rank correlations and coefficients of tail dependence.

Covers copula densities and tail dependence in quantitative risk management.

Introduces major statistical methodologies for uncovering associations between vector components in multivariate data.

Covers the fundamentals of Bayesian Networks and their applications in probabilistic topic modeling.