Lecture

Probability and Statistics: Fundamentals

Related lectures (30)

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

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

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

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

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

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

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

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

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

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

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

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.

Explores convergence in probability, concentration inequalities, laws of large numbers, and properties of distributions.

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

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.

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

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

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