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Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.

Estimation and Correlation

Explains estimation, correlation, and Pearson correlation in statistics, focusing on measuring and describing relationships between variables.

Stochastic Processes: Basics & Stationarity

Covers signal generation, statistical relations, stationarity, white noise, and orthogonality in stochastic processes.

Local Fields and Correlations in Ising Models

Covers local fields and correlations in Ising models and conformal field theory.

Stationarity in Stochastic Processes

Explores stationarity in stochastic processes, showcasing how statistical characteristics remain constant over time and the implications on random variables and Fourier transforms.

Variance, Covariance, and Correlation

Explores variance, covariance, and correlation in statistics, essential for data analysis.

Central Limit Theorem: Properties and Applications

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

Red bus/Blue bus paradox

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Parseval Relations and Fourier Transform

Covers Parseval relations, Fourier series, energy calculation, and correlation in signal processing.

Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.