https://github.com/alejoduarte23/si_bayesianmixturemodel

Implementation of a two-stage fast Bayesian system identification for separated Modes. This repository expands the usage of this technique by adding a mixture model fit to obtain modal parameters from the posterior distribution.
https://github.com/alejoduarte23/si_bayesianmixturemodel

matplotlib numpy scikit-learn scipy

Last synced: 3 months ago
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Implementation of a two-stage fast Bayesian system identification for separated Modes. This repository expands the usage of this technique by adding a mixture model fit to obtain modal parameters from the posterior distribution.

• Host: GitHub
• URL: https://github.com/alejoduarte23/si_bayesianmixturemodel
• Owner: AlejoDuarte23
• Created: 2024-08-11T03:53:04.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-08-13T03:41:47.000Z (5 months ago)
• Last Synced: 2024-09-23T06:03:27.758Z (3 months ago)
• Topics: matplotlib, numpy, scikit-learn, scipy
• Language: Python
• Homepage:
• Size: 167 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Bayesian Modal Updating For System Identification

Implementation of a two-stage fast Bayesian system identification for separated nodes. This repository expands the usage of this technique by adding a mixture model fit to obtain modal parameters from the posterior distribution. This is not a peer reviewed apporach just and implementation that I made during a boring weekend.

![Mixture Result](results/mixture_result.svg)

### Get the Data

Create a "data" folder and download the dataset from the following link:

[Download Dataset](https://1drv.ms/f/c/ad540b8f16531ec4/Eg9_JhlPvXpHo-eqEOAl0tkBB0tP4tBCq_-2xiE-sWpjPw?e=RlCKAG)