Estimating R: Moments and Covariance

Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.

Explores principal components, covariance, correlation, choice, and applications in data analysis.

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

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

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.