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Explores independence and covariance between random variables, discussing their implications and calculation methods.
Covers various methods for estimating model parameters, such as method of moments and maximum likelihood estimation.
Covers hypothesis testing, Neyman-Pearson lemma, ROC curves, and optimal tests with examples and simulations.
Explores combinatorial mathematics, covering permutations, combinations, and binomial coefficients, along with probability and statistics concepts.
Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.
Covers continuous random variables, probability density functions, and distributions, with practical examples.
Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.
Explores modes of convergence in probability and statistics, illustrating concepts with examples and discussing the continuity theorem.
Covers the multinomial distribution, joint density, marginal distribution, and conditional distribution.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Covers fundamental concepts in probability and statistics, including the law of total probability, Bayes' theorem, and independence of events.
