By restricting to Gaussian distributions the optimal Bayesian filtering problem can be
transformed into an algebraically simple form which allows for computationally efficient
algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians
only (2) Gaussian mixture filtering for strong nonlinearities (3) Gaussian process filtering
for purely data-driven scenarios. For each setting efficient algorithms are derived and
applied to real-world problems.
