Quantitative Risk Management: Copulas and Generative Adversarial Networks

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Page 2 of 4

Explores common probability distributions, special distributions, and entropy concepts.

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Covers rank correlations, tail dependence, and copula fitting methods.

Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Covers the normal distribution, including its properties and calculations.

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Explores max entropy, Shannon's entropy, Lagrange multipliers, and Monte Carlo sampling techniques.