Multiplicative Latent Force Models
This work addresses the problem of flexible Bayesian modeling for dynamic systems, offering a novel extension that is incremental in nature.
The authors tackled the challenge of modeling dynamic systems by extending latent force models to include multiplicative interactions between Gaussian processes and latent states, which improved trajectory geometry control, but required an approximation for inference, validated through a simulation study.
Bayesian modelling of dynamic systems must achieve a compromise between providing a complete mechanistic specification of the process while retaining the flexibility to handle those situations in which data is sparse relative to model complexity, or a full specification is hard to motivate. Latent force models achieve this dual aim by specifying a parsimonious linear evolution equation which an additive latent Gaussian process (GP) forcing term. In this work we extend the latent force framework to allow for multiplicative interactions between the GP and the latent states leading to more control over the geometry of the trajectories. Unfortunately inference is no longer straightforward and so we introduce an approximation based on the method of successive approximations and examine its performance using a simulation study.