檢測與估計理論
Detection and Estimation Theory
| 節 | 週二 |
|---|---|
N 12:20–13:10 | 檢測與估計理論 ED203（光復） 3 節連堂 |
5 13:20–14:10 | |
6 14:20–15:10 |
* 根據陽明交大上課時間表所列
The course introduces the fundamentals of estimation and detection theories, and will emphasize the estimation part. Course content will cover the concepts of Minimum variance unbiased estimation, Cramer-Rao lower bound, sufficient statistics, Best linear unbiased estimation, Maximum likelihood estimation, least squared estimation, EM algorithm, Maximum a posteriori estimation, Linear minimum mean-squared error estimation, Kalman filter,particle filter and the Monte Carlo methods for estimation.
Linear Algebra Basic knowledge on Probability Theory
無備註
Lecture notes will be made available on the class website at http://140.113.144.123
Homework : 30% Midterms: 40% Final projects: 30%
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Minimum variance unbiased (MVU) estimation I: 1. Unbiased estimators |
| 第 2 週 | Minimum variance unbiased (MVU) estimation II: 2. Minimum variance cirterion 3. Existence of the MVU estimator |
| 第 3 週 | Cramer-Rao lower bound (CRLB): 1. Estimator accuracy consideration 2. Cramer-Rao lower bound (CRLB) 3. CRLB for signals in white Gaussian noise |
| 第 4 週 | General MVU estimators I: 1. Sufficient statistics 2. Finding sufficient statistics |
| 第 5 週 | General MVU estimators II: 1. Using sufficiency to find the MVU estimator |
| 第 6 週 | Best linear unbiased estimation (BLUE): 1. Finding the BLUE 2. Extension to a vector parameter |
| 第 7 週 | Maximum likelihood estimation (MLE) I: 1. Finding the MLE 2. MLE of the transformed parameters |
| 第 8 週 | Maximum likelihood estimation (MLE) II: 3. Asymptotic MLE |
| 第 9 週 | Midterm Examination |
| 第 10 週 | Recursive Estimation Algorithms 1. Steepest descent method 2. The scoring method 3. The Gauss-Siedel Method 4. The Expectation Maximization (EM) algorithm 5. Applications of the EM algorithm |
| 第 11 週 | Lease squared (LS) estimation: 1. Linear lease squared estimation 2. Recursive lease squared estimation 3. Constrained lease squared estimation |
| 第 12 週 | The Bayesian Philosophy: 1. Prior knowledge and estimation 2. The Bayesian linear model 3. Properties of Gaussian PDF |
| 第 13 週 | Maximum a posteriori (MAP) estimation: 1. The MAP estimator 2. The inimum mean squared error (MMSE) estimator 3. The medium estimator |
| 第 14 週 | Linear Minimum mean squared error (MMSE) estimation: 1. Linear MMSE estimators 3. Sequential Linear MMSE estimator |
| 第 15 週 | Kalman filters: 1. Kalman filter 2. Kalman versus Winner filter |
| 第 16 週 | Sampling methods for Estimation I: 1. The Monte Carlo sampling methods 2. The Metropolis-Hastings method |
| 第 17 週 | Sampling methods for Estimation II: 1. Particle filtering Final Project Proposal |
| 第 18 週 | Final Project Presentation |
Fundamentals of Statistical Signal Processing: Estimation Theory (Vol I) by Steven M. Kay
- 地點
- ED832
- 時間
- Mon. EF
- 聯絡方式
- Ext. 54531 Email: sauhsuan@cm.nctu.edu.tw