Lectures related to Normally distributed and uncorrelated does not imply independent

Explores statistical independence, Gaussian Mixture Models, and fitting data with Gaussian functions.

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Explains errors in correlated and uncorrelated sampling, correlation function, time, and blocking analysis.

Explores Monte Carlo integration through uncorrelated sampling and its error scaling with the number of points used.

Explores errors in sampling, correlation functions, time analysis, and blocking techniques.

Explores the optimality of the Least Squares Estimator and its large sample distribution.

Explores Moran's I for assessing spatial clustering in geographical data using precipitation as an example.

Explores independence and covariance between random variables, discussing their implications and calculation methods.