# Defining dynamics via Gaussian processes

September 18, 2019 — September 18, 2019

Two classic flavours together, Gaussian Processes and dynamical_systems, where the dynamics are modelled by a Gaussian process.

Here we use Gaussian processes to define the dynamics, in particular to learn nonparametric transition, observation or state densities. This is what (Turner, Deisenroth, and Rasmussen 2010; Frigola, Chen, and Rasmussen 2014; Frigola et al. 2013; Eleftheriadis et al. 2017) do.

This is distinct from calculating a Gaussian process posterior via a state filter, which is another way you can combine the concepts of *dynamics* and *Gaussian process*.

c.f. Variational filters and particle filters.

Possible the same, *recurrent Gaussian Processes*? 🏗 (Mattos et al. 2016, 2017; Föll et al. 2017).

## 1 References

*Advances in Neural Information Processing Systems 30*.

*arXiv:1711.00799 [Stat]*.

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*Advances in Neural Information Processing Systems 26*.

*Pattern Recognition Letters*.

*Proceedings of ICLR*.

*Journal of Process Control*, DYCOPS-CAB 2016,.

*arXiv:2104.14987 [Stat]*.

*International Conference on Machine Learning*.

*2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP ’03).*

*Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics*.

*ICML*.