https://github.com/acerbilab/normalizing-flow-regression
Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations
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Repository
Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations
Basic Info
- Host: GitHub
- Owner: acerbilab
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://acerbilab.github.io/normalizing-flow-regression/
- Size: 8.44 MB
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Metadata Files
README.md
Normalizing Flow Regression
This repository provides the implementation and code used in the AABI 2025 (proceedings track) article Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations (Li et al., 2025).
- See the paper web page for more information.
- The full paper is available on arXiv and as Markdown files.
Overview
Bayesian inference with computationally expensive likelihood evaluations remains a significant challenge in many scientific domains. We propose normalizing flow regression (NFR), a novel offline inference method for approximating posterior distributions. Unlike traditional surrogate approaches that require additional sampling or inference steps, NFR directly yields a tractable posterior approximation through regression on existing log-density evaluations.
Set up
```bash conda env create -f environment.yml conda activate nfr
install kernel for jupyter notebook
python -m ipykernel install --user --name nfr ```
See demo.ipynb for an example of using NFR.
Citation
To appear in 7th Symposium on Advances in Approximate Bayesian Inference (AABI 2025, proceedings track).
Li, C., Huggins, B., Mikkola, P., & Acerbi, L. (2025). Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations. In 7th Symposium on Advances in Approximate Bayesian Inference.
BibTeX
bibtex
@inproceedings{liNormalizingFlowRegression2025,
title = {Normalizing Flow Regression for {B}ayesian Inference with Offline Likelihood Evaluations},
booktitle = {7th Symposium on Advances in Approximate Bayesian Inference},
author = {Li, Chengkun and Huggins, Bobby and Mikkola, Petrus and Acerbi, Luigi},
year = {2025},
note = {To appear},
url = {https://approximateinference.org/2025/},
}
Acknowledgements
This repository includes code adapted from the nflows library: https://github.com/bayesiains/nflows, originally developed by Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios.
We have modified nflows/transforms/autoregressive.py such that:
- When neural network parameters are zeros, the flow becomes the identity transform.
- The scale and shift transformation is constrained to a specified range.
Owner
- Name: acerbilab
- Login: acerbilab
- Kind: organization
- Location: Finland
- Website: www.helsinki.fi/machine-and-human-intelligence
- Repositories: 6
- Profile: https://github.com/acerbilab
Machine and Human Intelligence Research Group - University of Helsinki
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Dependencies
- cma *
- corner *
- einops *
- hydra-core *
- lightning *
- scikit-learn *
- torchtestcase *
- umnn *
- autoflake *
- black *
- flake8 *
- isort *
- matplotlib *
- numpy *
- pytest *
- pyyaml *
- tensorboard *
- torch *
- torchtestcase *
- tqdm *
- umnn *
- matplotlib *
- numpy *
- tensorboard *
- torch *
- tqdm *
- umnn *
- corner *
- dill *
- plum-dispatch *
- wandb *