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Related lectures (27)

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Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Introduces the key concepts of convexity and its applications in different fields.

Covers statistical concepts like probability, estimation, hypothesis testing, and confidence intervals for mathematicians.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Explores combinatorial mathematics, covering permutations, combinations, and binomial coefficients, along with probability and statistics concepts.

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Covers the construction of confidence intervals and approximate critical values in statistical inference.

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

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