Abstract In this work we deal with Bayesian smoothing for a time varying system, to find smoothing estimators of signals in present of noise. The smoothing procedure is re-estimating a signal after adding new observations, or in the light of more new observations. This study began with modeling the fixed point smoothing process using Bayesian updating process, and then using this model to find smoothing estimators for the wind speed in Erbil city at the fixed point (t=15), for three month's (October, November, December) using (MATLAB 7). The results showed that the variances are reduced by adding any new observation; this demonstrates that the method works effectively.
I. Mawlood,K . (2012). Bayesian Smoothing of Discrete -Time Signals with Application. Journal of Kirkuk University For Administrative and Economic Sciences, 2(2), 254-267.
MLA
I. Mawlood,K . "Bayesian Smoothing of Discrete -Time Signals with Application", Journal of Kirkuk University For Administrative and Economic Sciences, 2, 2, 2012, 254-267.
HARVARD
I. Mawlood K. (2012). 'Bayesian Smoothing of Discrete -Time Signals with Application', Journal of Kirkuk University For Administrative and Economic Sciences, 2(2), pp. 254-267.
CHICAGO
K I. Mawlood, "Bayesian Smoothing of Discrete -Time Signals with Application," Journal of Kirkuk University For Administrative and Economic Sciences, 2 2 (2012): 254-267,
VANCOUVER
I. Mawlood K. Bayesian Smoothing of Discrete -Time Signals with Application. Journal of Kirkuk University For Administrative and Economic Sciences. 2012;2(2):254-267.
