Basics of Risk Analysis and Management

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Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Covers various probability distributions and their applications in real-world scenarios.

Introduces random variables, probability distributions, and expected values through practical examples.

Explores continuous random variables, density functions, and distribution laws with practical examples.

Explores moment-generating functions, linear combinations, and normality of random variables.

Covers Monte-Carlo integration, simulation, and transforming draws from different distributions.

Covers common distributions, moment generating functions, and covariance matrices in statistics for data science.

Explores joint random variables, conditional density, and independence in probability and statistics.

Covers random variables, probability functions, expectations, variances, and joint distributions.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.