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The partitioned estimation algorithms of Lainiotis for the linear continuous-time state estimation problem have been generalized in this paper in two important ways. First, the initial condition of the estimation problem can, using the results of this paper, be partitioned into the sum of an arbitrary number of jointly Gaussian random variables; and second, these jointly Gaussian random variables may be statistically dependent. The form of the resulting algorithm consists of an imbedded Kalman filter with partial initial conditions and one correction term for each other partition or subdivision of the initial state vector. Emphasis in this paper is on ways in which this approach, called multipartitioning, can be used to provide added insight into the estimation problem. One significant application is in the parameter identification problem where identification algorithms can be formulated in which the inversion of the information matrix of the parameters is replaced by simple division by scalars. A second use of multipartitioning is to show the specific effects on the filtered state estimate of off-diagonal terms in the initial-state covariance matrix.