johannesulf / nautilus
Neural Network-Boosted Importance Nested Sampling for Bayesian Statistics
Why this job: works in the areas we cover, but does not say which job it does
What it does
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.
Get started
pip install nautilus-sampler- Maintenance
- Active
- Last commit 7 days ago
- Stars
- 118
- On GitHub
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v1.0.6
- Released 10 months ago
- Built with
- Python
- 100% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 91% closed
- 4 still open
- Pull requests
- 35 merged
- 0 waiting
- Releases
- 18
- 1 in the last year
- Contributing guide (opens in new tab)
- No security policy
- Code of conduct (opens in new tab)
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More: languages, topics and project record
Languages
- Python 100%
Topics
- bayesian-statistics
- inference
- machine-learning
- python
- sampling
Project record
- JobOther AI tools
- StatusListed
- Commits164
- Forks22
- Watching6
- Stars in 28 days+2
- CreatedAug 23, 2021
- First seen by usNov 7, 2026
- CheckedNov 8, 2026
