Gaussian Distribution Basics

Related lectures (29)

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Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

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

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

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

Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.

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

Covers inequalities, joint Gaussian distribution, risk estimation, and classification method testing in probability and statistics.

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

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

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.