Introduces maximum likelihood estimation for statistical parameter estimation, covering bias, variance, and mean squared error.
Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.
Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.
Explores statistical estimation, comparing estimators based on mean and variance, and delving into mean squared error and Cramér-Rao bound.
Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.
