Parameter inference in mechanistic models of
coupled differential equations is a topical problem.
We propose a new method based on kernel
ridge regression and gradient matching, and
an objective function that simultaneously encourages
goodness of fit and penalises inconsistencies
with the differential equations. Fast minimisation
is achieved by exploiting partial convexity
inherent in this function, and setting up an iterative
algorithm in the vein of the EM algorithm.
An evaluation of the proposed method on various
benchmark data suggests that it compares
favourably with state-of-the-art alternatives
