  The factor graph approach to discrete-time linear Gaussian state space models
is well developed. The paper extends this approach to continuous-time linear
systems/filters that are driven by white Gaussian noise. By Gaussian message
passing, we then obtain MAP/MMSE/LMMSE estimates of the input signal, or of the
state, or of the output signal from noisy observations of the output signal.
These estimates may be obtained with arbitrary temporal resolution. The
proposed input signal estimation does not seem to have appeared in the prior
Kalman filtering literature.
