EC874 Detection, Estimation and Statistical Learning Theory
Course Name:
EC874 Detection, Estimation and Statistical Learning Theory
Programme:
M.Tech(SPML)
Category:
Elective (Ele)
Credits (L-T-P):
(3-0-2) 4
Content:
Non-Bayesian Detection: Hypothesis test, Neyman-Pearson lemma, Likelihood ratio test, Kullback- Leiblerdivergence, Matched filter, Sequential test; Minimum mean square-error (MMSE), Linear MMSE, Minimum probability of error (MAP), Stationarity and power spectral density, Wiener filter, Kalman filter, Non-Bayesian Estimation: Sufficient Statistic, Bias, Minimum variance unbiased estimator. Cramer-Rao bound. Maximum likelihood, Expectation maximization, Bayesian inference, empirical risk minimization, concentration inequalities, PAC learning, nonparametric inference.
References:
HV. Poor, AnIntroduction to Signal Detection and Estimation, 2nd Ed., Springer-Verlag, 1994.
HL. Van Trees, Detection, Estimation and Modulation Theory, Parts 1and 2, John WileyInter-Science.
SM. Kay, Fundamentals of Statistical Signal Processing, vol 1and vol 2, Prentice Hall, 1993.
Kailath, sayed, and Hassibi, Linear Estimation, Prentice Hall, 2000.
MD,Srinath and PK. Rajasekharan, An Introduction to Statistical Signal Processing with Applications, John Wiley &
Sons, 1979.
LL. Scharf, Statistical Signal Processing; Detection, Estimation, and Time Series Analysis, Addison-Wesley, 1991.
Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie, The Elements of Statistical Learning: Data Mining,
Inference, and Prediction, Springer, 2009.
Department:
Electronics and Communication Engineering(ECE)