Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.
Explores hypothesis testing in statistics, focusing on decision-making based on sample data and controlling error probabilities.
Covers t-tests, p-values calculation, and comparison of coefficients.
Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.
Covers hypothesis testing, Neyman-Pearson lemma, ROC curves, and optimal tests with examples and simulations.
Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.
