Extreme Problems in Diophantine Approximation and Dynamics

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

Explores uniform integrability, convergence theorems, and the importance of bounded sequences in understanding the convergence of random variables.

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

Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.

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

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Explores conditional density, expectations, and independence of random variables with practical examples.

Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.