Title :
Smoothing estimation of stochastic processes: Change of initial condition formulas
Author_Institution :
Harvard University, Cambridge, MA, USA
fDate :
1/1/1984 12:00:00 AM
Abstract :
By posing the change of initial condition (CIC) problem in the theory of smoothing in linear estimation in a setting stripped of all inessentials, simple, insightful derivations of most CIC formulas (and a new likelihood formula) are provided. Specifically, the CIC or partitioning problem is one of low rank perturbation to a covariance kernel and the formulas are simple consequences of inversion formulas for fixed rank modification of a positive definite kernel or matrix. The present derivation basically handles the discrete, continuous-discrete, and continuous cases at once: previous derivations had treated the discrete and continuous separately. The continuous-discrete results are new.
Keywords :
Smoothing methods; Stochastic processes; Bars; Covariance matrix; Kernel; Least squares approximation; Mathematics; Random variables; Smoothing methods; State-space methods; Statistics; Stochastic processes;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.1984.1103366