Statistical Theory: Fundamentals

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Related lectures (29)

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Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Probability and Statistics: Fundamental Theorems

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Elements of Statistics

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

Introduction to Inference

Covers the basics of probability theory, random variables, joint probability, and inference.

Probabilities and Statistics: Key Theorems and Applications

Discusses key statistical concepts, including sampling dangers, inequalities, and the Central Limit Theorem, with practical examples and applications.

Probability Theory: A Review

Offers a review of key probability concepts, including probability spaces, events, and random variables.

Generalized Linear Models

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Probability and Statistics

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

Random Variables: Expectation and Independence

Explores random variables, expectation, and independence in probability theory.

Conditional Density and Expectation

Covers conditional density, independence of random variables, expectation, and variance calculation.