Large numbers

Related lectures (32)

Page 3 of 4

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.

Explores the extension of the weak law of large numbers using St. Petersburg's paradox as an example.

Explores the Central Limit Theorem, emphasizing the convergence towards a Gaussian distribution.

Explores variance, covariance, and practical applications in statistics and probability.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Explores fundamental properties of groups and numbers, emphasizing equivalence classes and subgroup concepts.

Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.

Explores the convergence of random variables, the law of large numbers, and the distribution of failure time.

Explores Maximum Likelihood Estimation, covering assumptions, properties, distribution, shrinkage estimation, and loss functions.

Covers the concept of convergence in distribution for random variables and its significance in probability theory.