acerbilab/pyvbmc
PyVBMC: Variational Bayesian Monte Carlo algorithm for posterior and model inference in Python
This tool helps scientists, engineers, and researchers analyze complex models by estimating the probability distribution of their model parameters and evaluating model evidence for selection. You provide your model's computational definition, and it delivers insights into parameter uncertainties and a score for comparing different models, even when calculations are expensive or noisy. It's ideal for anyone working with computationally intensive simulations or black-box models in scientific research.
124 stars.
Use this if you need to understand the underlying parameters of your complex model or compare different models, especially when your model takes a long time to run or produces noisy results.
Not ideal if your model's equations are fully analytical and simple to compute, as more specialized probabilistic programming tools would be more efficient.
Stars
124
Forks
10
Language
Python
License
BSD-3-Clause
Category
Last pushed
Mar 09, 2026
Commits (30d)
0
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