Sequential estimation As each block of data is processed, the local parameters, x, can be dropped and the covariance matrix of the global parameters xg passed to the next estimation stage Total size of adjustment is at maximum the number of global parameters plus loca parameters needed for the data being processed at the moment, rather than all of the local parameters 03/1703 12540Lec11 Summary We examined the way covariance matrices and be manipulated Estimation from a statistical point of view Sequential estimation Next class continue with sequential estimation in terms of Kalman Filtering Reminder: Paper topic and outline due Wednesday 12540Lec1103/17/03 12.540 Lec 11 19 Sequential estimation parameters, xl xg 03/17/03 12.540 Lec 11 20 • As each block of data is processed, the local , can be dropped and the covariance matrix of the global parameters passed to the next estimation stage. • Total size of adjustment is at maximum the number of global parameters plus local parameters needed for the data being processed at the moment, rather than all of the local parameters. Summary • We examined the way covariance matrices and be manipulated • Estimation from a statistical point of view • Sequential estimation. • Next class continue with sequential estimation in terms of Kalman Filtering. • Reminder: Paper topic and outline due Wednesday. 10