aurelio-amerio/GenSBI
Generative Models for Simulation-Based Inference in JAX
This tool helps researchers and practitioners analyze complex systems where direct mathematical models are unavailable or too difficult to compute. By taking simulation outputs and the parameters used to generate them, it helps you understand the underlying parameters that best explain your real-world observations. It's ideal for scientists and analysts who rely on simulations but need to infer unknown parameters from their experimental data.
Available on PyPI.
Use this if you need to determine the probable values of internal parameters that led to observed outcomes from a simulation, especially when the underlying mathematical likelihood is too complex to write down directly.
Not ideal if you have a simple, well-defined mathematical model for which you can easily calculate the likelihood function directly.
Stars
8
Forks
—
Language
Python
License
—
Category
Last pushed
Mar 16, 2026
Commits (30d)
0
Dependencies
17
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/aurelio-amerio/GenSBI"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
yang-song/score_sde_pytorch
PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential...
ermongroup/ncsnv2
The official PyTorch implementation for NCSNv2 (NeurIPS 2020)
yang-song/score_sde
Official code for Score-Based Generative Modeling through Stochastic Differential Equations...
amazon-science/unconditional-time-series-diffusion
Official PyTorch implementation of TSDiff models presented in the NeurIPS 2023 paper "Predict,...
AI4HealthUOL/SSSD-ECG
Repository for the paper: 'Diffusion-based Conditional ECG Generation with Structured State Space Models'