Variational_Gradient_Matching_for_Dynamical_Systems

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Variational_Gradient_Matching_for_Dynamical_Systems

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Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"

Variational_Gradient_Matching_for_Dynamical_Systems 최신버젼 다운로드

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# Variational Gradient Matching for Dynamical Systems

<p align="center">
  <a href="https://ngorbach.github.io/Variational_Gradient_Matching_for_Dynamical_Systems/#17"><b>code documentation</b></a> |
  <a href="https://papers.nips.cc/paper/7066-scalable-variational-inference-for-dynamical-systems.pdf"><b>NIPS 2017 conference publication</b></a> |
  <a href="https://arxiv.org/abs/1705.07079"><b>arXiv version</b></a> |
  <a href="https://www.research-collection.ethz.ch/handle/20.500.11850/261734"><b>doctoral thesis</b></a> |
<a href="https://papers.nips.cc/paper/7274-efficient-and-flexible-inference-for-stochastic-systems.pdf"><b>supplementary paper</b></a>
 </p>

## Contents

Sample code for the NIPS (2018) paper <a href="https://papers.nips.cc/paper/7066-scalable-variational-inference-for-dynamical-systems.pdf"><b>Scalable Variational Inference for Dynamical Systems</b></a> by Nico S. Gorbach, Stefan Bauer and Joachim M. Buhmann. Please cite our paper if you use our program for a further publication. The derivations of the formulas used in this code are also given in this <a href="https://www.research-collection.ethz.ch/handle/20.500.11850/261734"><b>doctoral thesis</b></a> as well as in parts of <a href="https://arxiv.org/pdf/1804.04378.pdf"><b>Wenk et al. (2018)</b></a>.

Run the Python scripts *VGM_for_Lotka_Volterra.py* or *VGM_for_Lorenz_attractor.py*. Alternatively, you can also run the Jupyter Notebook scripts *VGM_for_Lotka_Volterra.ipynb* or *VGM_for_Lorenz_attractor.ipynb*.
</p>
<hr>

## Some Results

### Lotka-Volterra

![Alt text](https://raw.githubusercontent.com/ngorbach/Variational_Gradient_Matching_for_Dynamical_Systems/master/docs/sample_results/VGM_for_Lotka_Volterra.png)

### Lorenz 96

![Alt text](https://raw.githubusercontent.com/ngorbach/Variational_Gradient_Matching_for_Dynamical_Systems/master/docs/sample_results/VGM_for_Lorenz96.png)

### Lorenz Attractor

![Alt text](https://raw.githubusercontent.com/ngorbach/Variational_Gradient_Matching_for_Dynamical_Systems/master/docs/sample_results/VGM_for_Lorenz_Attractor.png)

### Dynamic Causal Modeling

![Alt text](https://raw.githubusercontent.com/ngorbach/Variational_Gradient_Matching_for_Dynamical_Systems/master/docs/sample_results/VGM_for_dynamic_causal_modeling.png)

<hr>

## Authors

**Nico Stephan Gorbach** and **Stefan Bauer**, email: nico.gorbach@gmail.com

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