Scalable Bayesian Modelling: An updated benchmark
For a detailed understanding of this work, its motivation and next steps, please refer to this blog post.
conda env create -f environment.yamlconda activate sbmIn the folder notebooks, you can find the file template.ipynb where you can add the code to get your data and models to create a benchmark for your specific use case. Cells preceded by the message ✍🏽 User input required should be filled, the other cells can be optionally modified according to your needs.
The sampling results are saved by default in the path data/results.
The folder also has the file example.ipynb, with an example using the template.
The template can be executed in Google Colab. Before executing the code, follow these steps:
- Change runtime in
Runtime > Change runtime typeif you want to execute the notebook using GPU. - Uncomment the first cell which makes sure Colab has the correct versions and required files.
- Set the variable
output_pathtodata/resultsor to a folder you know exists in the environment.
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