Bayes' Theorem: Probability Applications

Related lectures (29)

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

Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.

Covers examples of probability, including Bayes Theorem, independence, and conditional probability.

Explores the Law of Total Probability and its applications in real-world scenarios, introducing key concepts in probability theory.

Covers conditional probability distributions and introduces the concept of conditional expected value.

Covers conditional probability, illustrating how events influence each other's probabilities and its applications in real-world scenarios.

Introduces fundamental probability concepts, including events, complements, conditional probability, and random variables.

Explains conditional probability through dice and coin examples.

Covers the Expectation Maximization algorithm for learning parameters and dealing with unknown variables.