Overview¶
Nautilus is an MIT-licensed pure-Python package for Bayesian posterior and evidence estimation. It utilizes importance sampling and efficient space exploration using neural networks. Compared to traditional MCMC and Nested Sampling codes, it often needs fewer likelihood calls and produces much larger posterior samples. Additionally, nautilus is highly accurate and produces Bayesian evidence estimates with percent precision. It is widely used in many areas of astrophysical research.
User Guide
Discussion
API Documentation
Support¶
If you are encountering issues with nautilus, please raise an issue on the nautilus GitHub page. If you have suggestions to improve this tutorial or would like to request features, you can use the same procedure or reach out to the authors.
Attribution¶
A paper describing nautilus’s underlying methods and performance has been published in the Monthly Notices of the Royal Astronomical Society. A draft of the paper is also available on arXiv. Please cite the paper if you find nautilus helpful in your research.
@article{nautilus,
author = {Lange, Johannes U},
title = "{nautilus: boosting Bayesian importance nested sampling with deep learning}",
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {525},
number = {2},
pages = {3181-3194},
year = {2023},
month = {08},
doi = {10.1093/mnras/stad2441},
url = {https://doi.org/10.1093/mnras/stad2441},
eprint = {https://academic.oup.com/mnras/article-pdf/525/2/3181/51331635/stad2441.pdf},
}
License¶
The project is licensed under the MIT license. The logo uses an image from the Illustris Collaboration.