Multivariate Statistics: Conditional Distributions

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Page 2 of 4

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Covers Gaussian Mixture Regression principles, modeling joint and conditional densities for multimodal datasets.

Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.

Covers correlation, covariance, empirical estimates, eigenvalues, normality testing, and factor models.

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

Introduces major statistical methodologies for uncovering associations between vector components in multivariate data.

Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.

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

Covers probability, statistics, independence, covariance, correlation, and random variables.