Statistical inference for dynamical systems: a review
arXiv:1204.6265
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 (10)
- Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
- Approximate Bayesian computation (ABC) gives exact results under the assumption of model error
- Bayesian Nonparametric Inference of Switching Linear Dynamical Systems
- Sequential Monte Carlo smoothing with application to parameter estimation in non-linear state space models
- Use of synchronization and adaptive control in parameter estimation from a time series
- Better Nonlinear Models from Noisy Data: Attractors with Maximum Likelihood
- On Statistical Methods of Parameter Estimation for Deterministically Chaotic Time-Series
- Statistical Consequences of Devroye Inequality for Processes. Applications to a Class of Non-Uniformly Hyperbolic Dynamical Systems
- Chain ladder method: Bayesian bootstrap versus classical bootstrap
- Devroye Inequality for a Class of Non-Uniformly Hyperbolic Dynamical Systems