Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Covers the multinomial distribution, joint density, marginal distribution, and conditional distribution.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Explores market response functions, flash crashes, correlation estimation, and noise filtering in finance.
