Particle filter-based Gaussian process optimisation for parameter inference
This is an incremental improvement for researchers in statistical inference, offering a heuristic trade-off in surrogate modeling.
The authors tackled parameter inference in nonlinear/non-Gaussian state space models by proposing an iterative method combining particle filters and Gaussian processes, achieving good performance in accuracy and computational cost on two models.
We propose a novel method for maximum likelihood-based parameter inference in nonlinear and/or non-Gaussian state space models. The method is an iterative procedure with three steps. At each iteration a particle filter is used to estimate the value of the log-likelihood function at the current parameter iterate. Using these log-likelihood estimates, a surrogate objective function is created by utilizing a Gaussian process model. Finally, we use a heuristic procedure to obtain a revised parameter iterate, providing an automatic trade-off between exploration and exploitation of the surrogate model. The method is profiled on two state space models with good performance both considering accuracy and computational cost.