  Recently Whitaker et al. (2017) considered Bayesian estimation of diffusion
driven mixed effects models using data-augmentation. The missing data,
diffusion bridges connecting discrete time observations, are drawn using a
"residual bridge construct". In this paper we compare this construct (which we
call residual proposal) with the guided proposals introduced in Schauer et al.
2017. It is shown that both approaches are related, but use a different
approximation to the intractable stochastic differential equation of the true
diffusion bridge. It reveals that the computational complexity of both
approaches is similar. Some examples are included to compare the ability of
both proposals to capture local nonlinearities in the dynamics of the true
bridge.

Computation