Abstract
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 with 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.
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 with 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.
| Original language | English |
|---|---|
| Title of host publication | Bayesian Statistics: New Challenges and New Generations - BAYSM 2018 |
| Publisher | Springer |
| Pages | 53-61 |
| Number of pages | 9 |
| DOIs | |
| Publication status | E-pub ahead of print - 22 Nov 2019 |
