11.2 Parameter estimation in WGN Assume k samples of the measured signal are y;=s;(a)+n,i=1,...,k where the noise is zero-mean,white Gaussian noise with variance o2.Therefore, 心e7齐-ej MAP.p可mx→p(ik)+小&np(a)-0 UESTC 是立(g-aa@+npa=011.2 Parameter estimation in WGN Assume k samples of the measured signal are where the noise is zero-mean, white Gaussian noise with variance . Therefore, MAP: 3 UESTC ( ) , 1, , i i i y n = + = s i k α 2  ( ) ( ) ( ( )) 2 / 2 2 2 1 1 1 exp 2 2 k k i i i p y y s     =   = − −      ln ln 0 p y p (   ) ( )     + =   p y ( ) max ( ( )) ( ) ( ) 2 1 1 ln 0 k i i i i s y s p       =   − + =   