Probability Fundamentals

Related lectures (30)

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Covers the fundamentals of probability theory, including conditional probability, Bayes' rule, and random variables.

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

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

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

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

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

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

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

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

Covers the normal distribution, including its properties and calculations.