Fast, parallel and lightweight simulation-based inference in JAX.
Project description
sbiax
Fast, lightweight and parallel simulation-based inference.
sbiax is a lightweight library for simulation-based inference (SBI) with a fixed-grid of simulations.
The design puts the neural density estimator (NDE) models at the centre of the code, allowing for flexible combinations of different models.
[!WARNING] :building_construction: Note this repository is under construction, expect changes. :building_construction:
Design
A typical inference with SBI occurs with
- fitting a density estimator to a set of simulations and parameters $(\xi, \pi)$ that may be compressed to summary statistics,
- the measurement of a datavector $\hat{\xi}$,
- the sampling of a posterior $p(\pi|\hat{\xi})$ conditioned on the measurement $\hat{\xi}$.
sbiax is designed to perform such an inference.
Usage
Install via
pip install sbiax
and have a look at examples.
Project details
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