Since a closed form solution to the Bayesian recursive estimation is available only for a few special cases , such as the linear Gaussian system
(which leads to the classical standard Kalman filter), a suboptimal solution is a preferable choice in the general case [4, 5].
Kalman filter based data assimilation algorithms are optimal for the assimilation of linear Gaussian systems
. The requirements of using Kalman filter based data assimilation algorithms are that the studied systems should be modeled in linear equations and the distributions of processing noises and observation noises are Gaussian.
More specific topics include vectors and operators, composite systems and entanglement, the classical-quantum channel, quantum entropy and information quantities, the transmission of quantum information, and Gaussian systems