https://github.com/panos108/gaussian-process-scenario-propagation-with-gpy
https://github.com/panos108/gaussian-process-scenario-propagation-with-gpy
dynamic-systems gaussian-processes gpy uncertainty-propagation
Last synced: 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/panos108/gaussian-process-scenario-propagation-with-gpy
- Owner: panos108
- Created: 2020-06-16T11:59:28.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-08-14T13:24:21.000Z (about 4 years ago)
- Last Synced: 2025-01-18T07:31:14.240Z (9 months ago)
- Topics: dynamic-systems, gaussian-processes, gpy, uncertainty-propagation
- Language: Python
- Homepage:
- Size: 13.7 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Gaussian-Process-Scenario-Propagation-with-GPy
Propagation of uncertainty using Gaussian process can be a challenging task. In this work, scenarios are sampled
from a Gaussian process, a more natural way to compute the moments in future instances (See [1]). GP packages like GPy [2] are great for automating Gaussian process regression, however they lack in uncertainty propagation.
This repo contain GPy implementation of Scenario-based uncertainty propagation using GPy, applied in a photo-production of phycocyanin synthesized by cyanobacterium Arthrospira platensis.
[1] J. Umlauft, T. Beckers and S. Hirche, Scenario-based Optimal Control for Gaussian Process State Space Models, 2018 European Control Conference (ECC), Limassol, 2018, pp. 1386-1392, doi: 10.23919/ECC.2018.8550458.
[2] GPy, GPy: A Gaussian process framework in python, 2018, http://github.com/SheffieldML/GPy