Learn · Statistical Signal Processing
Lecture 10 of 41Estimation Theory-II
Lec 10 : Cramer Rao Lower Bound 2
Prof. Prabin Kumar Bora · IIT Guwahati
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- Statistical Signal Processing, IIT Guwahati (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures
- Lec 1 : Overview of Statistical Signal Processing
- Lec 2 : Probability and Random Variables
- Lec 3 : Linear Algebra of Random Variables
- Lec 4 : Random Processes
- Lec 5 : Linear Shift Invariant Systems with Random Inputs
- Lec 6 : White Noise and Spectral Factorization Theorem
- Lec 7 : Linear Models of Random Signals
- Lec 8 : Estimation Theory 1
- Lec 9 : Estimation Theory 2: MVUE and Cramer Rao Lower Bound
- Lec 10 : Cramer Rao Lower Bound 2
- Lec 11 : MVUE through Sufficient Statistic
- Lec 12 : MVUE through Sufficient Statistic 2
- Lec 13 : Method of Moments and Maximum Likelihood Estimators
- Lec 14 : Properties of Maximum Likelihood Estimator (MLE)
- Lec 15 : Bayesian Estimators
- Lec 16 : Bayesian Estimators 2
- Lec 17 : Optimal linear filters: Wiener Filter
- Lec 18 : FIR Wiener filter
- Lec 19 : Non-Causual IIR Wiener Filter
- Lec 20 : Causal IIR Wiener Filter
- Lec 21: Linear Prediction of Signals 1
- Lec 22 : Linear Prediction of Signals 2
- Lec 23 : Linear Prediction of Signals 3
- Lec 24: Review Assignment 1
- Lec 25: Adaptive Filters 1
- Lec 26: Adaptive Filters 2
- Lec 27: Adaptive Filters 3
- Lec 28: Review Assignment 2
- Lec 29: Adaptive Filters 4
- Lec 30: Adaptive Filters 4(cont.)
- Lec 31: Review Assignment 3
- Lec 32: Recursive Least Squares (RLS) Adaptive Filter
- Lec 33: Recursive Least Squares (RLS) Adaptive Filter - 2
- Lec 34: Review Assignment 4
- Lec 35: Kalman Filter - 1
- Lec 36: Vector Kalman Filter
- Lec 37: Linear Models of Random Signals
- Lec 38: Review - 1
- Lec 39: Review - 2
- Live Session 09-02-2021
- Live Session 06-05-2020