Lectures related to Arithmetic mean

Covers ECDF, quantiles, mean, median, and box plots for dataset analysis.

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

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

Covers the properties of harmonic functions, focusing on the mean value property and the maximum principle.

Explores martingales, Brownian movement, mean numbers, and generation hypotheses.

Introduces weighted mean calculation and radioactive decay concept with probability estimation.

Covers the properties and error estimates of the Monte Carlo method in stochastic simulation.

Explores the convergence of averages of random variables and empirical frequencies in probability theory.

Explores the calculation of growth rates, average GDP growth, and environmental footprint changes.

Explains how to calculate average growth rates using arithmetic mean, geometric mean, and linear trend analysis.

Covers mean, median, mode, box plots, and histograms in datasets.

Explores the microcanonical ensemble applied to a perfect gas and the statistical mean of physical quantities.

Explains how constants affect the mean and variance of random numbers.

Explores Replica Symmetry Breaking in the Random Energy Model, focusing on configurations, variance, mean, and system behavior.

Covers broadcasting, operations, comparisons, and numpy constants like pi, e, and infinity.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Covers moments computation and probability distribution transformations.

Explores the Central Limit Theorem and its statistical implications in random variables.

Covers the concept of stationarity in continuous-time stochastic processes and its implications in communication systems.

Explains the Law of Large Numbers and its application to random variables.