Statistical inference for dynamical systems: A review

By K. McGoff, Sayan Mukherjee, Natesh S. Pillai. 2012

51 citations recorded by the source corpus.

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

Subfields: Artificial Intelligence, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Justin Bayer
2. Patrick van der Smagt
3. Maximilian Sölch
4. Maximilian Karl
5. Danijar Hafner
6. Yoshua Bengio
7. Jimmy Ba
8. Dit-Yan Yeung

In Control and Systems Engineering

1. Justin Bayer
2. Patrick van der Smagt
3. Maximilian Karl

Citations in the corpus

• Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data (2016, background): “poral dependencies, e.g., Graves (2013); Watter et al. (2015); Chung et al. (2015); Bayer & Osendorfer (2014). Time series for dynamic systems have been studied extensively in systems theory, cf. McGoff et al. (2015) and sources therein. In particular, state space models have shown to be a power”