  Parameter inference of dynamical systems is a challenging task faced by many
researchers and practitioners across various fields. In many applications, it
is common that only limited variables are observable. In this paper, we propose
a method for parameter inference of a system of nonlinear coupled ODEs with
partial observations. Our method combines fast Gaussian process based gradient
matching (FGPGM) and deterministic optimization algorithms. By using initial
values obtained by Bayesian steps with low sampling numbers, our deterministic
optimization algorithm is both accurate and efficient.
