(3 units). Binary, M-ary and composite hypothesis testing. Bayes risk and Neyman-Pearson criteria. Parameter estimation: Cramer-Rao bounds; maximum-likelihood estimation. Detection in additive white Gaussian noise and coloured noise. Noise in noise problems. Classical estimation problems. The linear filtering problem. Wiener/Kalman filtering. Sequential and non-parametric detection. This course is equivalent to EACJ 5503 at Carleton University. Course Component: Lecture
(3 units). Binary, M-ary and composite hypothesis testing. Bayes risk and Neyman-Pearson criteria. Parameter estimation: Cramer-Rao bounds; maximum-likelihood estimation. Detection in additive white Gaussian noise and coloured noise. Noise in noise problems. Classical estimation problems. The linear filtering problem. Wiener/Kalman filtering. Sequential and non-parametric detection. This course is equivalent to EACJ 5503 at Carleton University. Course Component: Lecture
