Functions Generators

Related lectures (30)

Page 3 of 3

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

Explores the Central Limit Theorem and its statistical implications in random variables.

Covers the basics of probability distributions, including mean, variance, and properties of random variables.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

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

Explores variance, covariance, and practical applications in statistics and probability.

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

Explores moment generating functions and multivariate normal distributions in probability and statistics.

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

Covers Cramer's theorem and Hoeffding's inequality in the context of the large deviations principle.