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Related lectures (32)

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All of Probability: LLN, CLT, Chernoff and PAC bound

Covers the Law of Large Numbers, Central Limit Theorem, Chernoff bounds, and PAC bounds in probability theory.

Integers: Elementary Concepts

Covers fundamental concepts related to integers, including properties of well-ordered sets and the principle of induction.

Monte Carlo Estimation: Error Analysis

Covers the Monte Carlo method for generating realizations and unbiased estimators.

Probabilities and Statistics: Key Theorems and Applications

Discusses key statistical concepts, including sampling dangers, inequalities, and the Central Limit Theorem, with practical examples and applications.

Stochastic Processes: Symmetric Random Walk

Covers the properties of the symmetric random walk in stochastic processes.

All of Probability: Basic Bounds, LLN & CLT

Introduces basic bounds, LLN, and CLT in probability theory, emphasizing convergence to normal distribution.

Central Limit Theorem: Empirical Mean

Explores the convergence of empirical mean distributions towards Gaussian distributions, focusing on the Central Limit Theorem.

Central Limit Theorem: Properties and Applications

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Kolmogorov's 0-1 Law: Convergence and Divergence

Explores Kolmogorov's 0-1 law, showcasing cases of convergence and divergence in random variables based on the finiteness of expectations.

Law of Large Numbers: Strong Convergence

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