Lectures related to Markov Chain: Configuration Sampling

Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.

Covers the estimation of distributions using samples and probability models.

Covers the criterion for recurrence in infinite chains based on the expected number of visits in a state.

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

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

Discusses Boltzmann, Bose-Einstein, and Fermi-Dirac statistics in statistical mechanics.