I’ve made my PhD thesis available on the research page, "Bayesian Learning of Continuous Time Dynamical Systems (with Applications in Functional Magnetic Resonance Imaging)". The thesis considers Bayesian filtering and smoothing for state and parameter estimation in general non-linear, non-Gaussian systems using stochastic differential models. It is the theoretical basis for impending updates to the dysii Dynamic Systems Library, including the kernel forward-backward and kernel two-filter smoothers, and distributed implementation of particle filters and kd trees.
I should note that this should be considered a draft version, as it is yet to be examined, and corrections may need to be made after it is. I’m providing it here mainly for the purposes of documenting dysii at this stage.