Topics to be covered:
- Introduction to the estimation theory and its applications
- Minimum Variance Unbiased Estimation
- Cramer-Rao Lower Bound
- Linear Models
- Best Linear Unbiased Estimators
- Maximum Likelihood Estimation
- Least Squares Estimation
- Minimum Mean Square Error (MMSE) Estimation
- General Bayesian Estimation
- Linear Bayesian Estimation
Reference:
"Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory" by Steven Kay