Statistical inference for dynamical systems: a review

Abstract

The topic of statistical inference for dynamical systems has been studied extensively across several fields. In this survey we focus on the problem of parameter estimation for non-linear dynamical systems. Our objective is to place results across distinct disciplines in a common setting and highlight opportunities for further research.

Some minor typos corrected

References in corpus (3)

• Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
• Sequential Monte Carlo smoothing with application to parameter estimation in non-linear state space models
• Chain ladder method: Bayesian bootstrap versus classical bootstrap