Explores Bayesian inference for Gaussian random variables, covering joint distribution, marginal pdfs, and the Bayes classifier.
Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.
Covers the multivariate normal distribution, properties, and sampling methods.
Explores principal components, covariance, correlation, choice, and applications in data analysis.
Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.
Introduces multivariate statistics, covering normal distribution properties and characteristic functions.
Covers random variables, covariance, and joint probability distributions.
Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.
Covers the multivariate normal distribution, moment-generating function, and combinatorics.
Explores Gaussian random vectors and their statistical properties, emphasizing the importance of specifying statistical properties in complex valued random vectors.
