Summary of Simulation-based Inference with the Python Package Sbijax, by Simon Dirmeier and Simone Ulzega and Antonietta Mira and Carlo Albert
Simulation-based inference with the Python Package sbijax
by Simon Dirmeier, Simone Ulzega, Antonietta Mira, Carlo Albert
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation (stat.CO); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces sbijax, a Python package for implementing state-of-the-art methods in neural simulation-based inference (SBI). SBI uses neural networks as surrogate models to overcome the limitations of traditional Bayesian inference with intractable likelihood functions. The sbijax package provides a user-friendly interface for constructing SBI estimators and computing posterior distributions. Additionally, it offers functionality for conventional approximate Bayesian computation, model diagnostics, and automatic estimation of summary statistics. The package’s implementation using JAX ensures high computational efficiency, enabling rapid training of neural networks and parallel execution on CPU and GPU. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new tool called sbijax that makes it easier to do something called neural simulation-based inference (SBI). SBI helps us figure out what might have happened in the past based on some data, even if we can’t directly measure everything. The tool uses special kinds of computer programs called neural networks to help with this task. It’s designed to be easy to use and fast, so you can get results quickly. The tool is useful for many fields, such as science and engineering. |
Keywords
» Artificial intelligence » Bayesian inference » Inference » Likelihood
