Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1699-1707, 2016.
Parameter inference in mechanistic models of coupled differential equations is a topical and challenging 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.