Monte Carlo Methods and Direct Integration in Civil Engineering

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Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Explores continuous random variables and their properties, including support and cumulative distribution functions.

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

Covers probability, statistics, independence, covariance, correlation, and random variables.

Explores conditional density, expectations, and independence of random variables with practical examples.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.

Covers the concept of property testing using statistical methods.