Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
Explores joint distributions, marginal laws, covariance, correlation, and variance properties.
Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
