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Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores the concept of entropy expressed in bits and its relation to probability distributions, focusing on information gain and loss in various scenarios.
Explores Poisson processes through examples and proofs, discussing customer arrivals in a store and related probabilities.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
