Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
Explores Gaussian random vectors and their statistical properties, emphasizing the importance of specifying statistical properties in complex valued random vectors.
Introduces random variables, probability measurement, expectation, moments, and relations between random variables.
Discusses Monte Carlo VaR accuracy, confidence intervals, backtesting, and multivariate distributions.
Explores joint distributions, marginal laws, covariance, correlation, and variance properties.
