Models defined by stochastic differential equations (SDEs) allow for the
representation of random variability in dynamical systems. The relevance of
this class of models is growing in many applied research areas and is already a
standard tool to model e.g. financial, neuronal and population growth dynamics.
However inference for multidimensional SDE models is still very challenging,
both computationally and theoretically. Approximate Bayesian computation (ABC)
allow to perform Bayesian inference for models which are sufficiently complex
that the likelihood function is either analytically unavailable or
computationally prohibitive to evaluate. A computationally efficient ABC-MCMC
algorithm is proposed, halving the running time in our simulations. Focus is on
the case where the SDE describes latent dynamics in state-space models; however
the methodology is not limited to the state-space framework. Simulation studies
for a pharmacokinetics/pharmacodynamics model and for stochastic chemical
reactions are considered and a MATLAB package implementing our ABC-MCMC
algorithm is provided.Comment: Version accepted for publication in Journal of Computational &
Graphical Statistic
