johannesulf/nautilus
Neural Network-Boosted Importance Nested Sampling for Bayesian Statistics
Estimating parameters and model evidence from observed data, especially in complex scientific models, can be very time-consuming. This tool takes your model and observed data as input and outputs highly accurate Bayesian posterior samples and evidence estimates. Researchers and scientists working with Bayesian statistics, particularly in fields like astrophysics, will find this useful for efficient and precise analysis.
102 stars.
Use this if you need to quickly and accurately determine the most probable parameters of a complex model given observational data, and also quantify the evidence for your model.
Not ideal if you are not working with Bayesian statistical methods or if your models are very simple and computationally inexpensive to explore with traditional methods.
Stars
102
Forks
14
Language
Python
License
MIT
Category
Last pushed
Dec 29, 2025
Commits (30d)
0
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