Part 1: Parameter Estimation |
- The statistical estimation problem. Performance metrics: bias, variance, MSE. Minimum Variance Unbiased Estimator (MVUE).
- Fisher Information and Cramer-Rao bound. Slepian-Bangs formula.
- Best Linear Unbiased Estimator (BLUE) and Maximum Likelihood Estimator (MLE): definition, properties, and examples. |
Part 2: Detection Theory |
- Hypothesis tests: types. Performance metrics: false positives and false negatives. ROC curves.
- Neyman-Pearson theorem: likelihood ratio.
- Detection under the Bayesian philosophy: probability of error, risk, optimum detector.
- Examples: deterministic and random signals |