Characterization of Stochastic Processes: Theory and Applications

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Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

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Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Covers Mc Diarmid's inequality and its applications in probability and martingale theory.

Covers probability, statistics, independence, covariance, correlation, and random variables.

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

Covers the basics of probability theory, random variables, joint probability, and inference.

Covers advanced probability concepts, including random variables and expected value calculation.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.