mlsanityData to 7 Oct 2026

Simulation-based Inference with the Python Package sbijax

arXiv 2409.19435 ↗First posted 28 Sep 2024cs.LG stat.CO stat.MLcited by 5

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Abstract (arXiv)

Neural simulation-based inference (SBI) describes an emerging family of methods for Bayesian inference with intractable likelihood functions that use neural networks as surrogate models. Here we introduce sbijax, a Python package that implements a wide variety of state-of-the-art methods in neural simulation-based inference using a user-friendly programming interface. sbijax offers high-level functionality to quickly construct SBI estimators, and compute and visualize posterior distributions with only a few lines of code. In addition, the package provides functionality for conventional approximate Bayesian computation, to compute model diagnostics, and to automatically estimate summary statistics. By virtue of being entirely written in JAX, sbijax is extremely computationally efficient, allowing rapid training of neural networks and executing code automatically in parallel on both CPU and GPU.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 29 of its 65 references that are in the map. First posted on arXiv 28 Sep 2024.

Builds on

29 of 65 references.

References · 29

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17 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2025Simulation-Based Inference: A Practical Guidecited by 30
  2. 2024sbi reloaded: a toolkit for simulation-based inference workflowscited by 23
  3. 2026BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Pythoncited by 7
  4. 2025Causal Posterior Estimationcited by 1
  5. 2025Fast and Robust Simulation-Based Inference With Optimization Monte Carlocited by 0