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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

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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

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