Multivariate t-distribution

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Related lectures (31)

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Discriminant Analysis: Bayes Rule

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Multivariate Statistics: Normal Distribution

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

Spherical & Elliptical Distributions

Covers spherical and elliptical distributions of random vectors.

Copulas: Properties and Applications

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

Gaussian Random Vectors: Conditional Generation

Explores generating Gaussian random vectors with specific components based on observed values and explains the concept of positive definite covariance functions in Gaussian processes.

Bayesian Inference: Gaussian Variables

Explores Bayesian inference for Gaussian random variables, covering joint distribution, marginal pdfs, and the Bayes classifier.

Multinomial Distribution

Covers the multinomial distribution, joint density, marginal distribution, and conditional distribution.

Copulas and Extreme Values

Explores copulas for measuring dependence strength in distributions and transforming variables to unit Fréchet margins.

Transformations of Joint Densities

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

Random Vectors & Distribution Functions

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