# Particle filter-based Gaussian process optimisation for parameter inference

arXiv: https://arxiv.org/abs/1311.0689 · HTML: https://mlsanity.com/paper/1311.0689/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2013.

## Built on by (most cited first, 1 of 1)

- [A Metropolis-Adjusted Langevin Algorithm for Sampling Jeffreys Prior](https://mlsanity.com/paper/2504.06372/) (2025)

## Abstract (arXiv)

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.