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Related lectures (29)

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Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.

Explores probability inequalities, convergence types, and moment generating functions for distribution approximation.

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

Explores discrete-time Markov chains, emphasizing first passage time probabilities and minimal solutions.

Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.

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

Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.

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

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