sbijax让复杂贝叶斯推断只需几行代码，高效易用。
Simulation-based Inference with the Python Package sbijax
- 基于JAX的神经网络推断框架，支持多种前沿方法
- 可快速构建后验分布，自动估计摘要统计量
- 适合需要高效贝叶斯推断的科研与工程人员
神经仿真推断（SBI）是一类用于似然函数不可解析情形下的贝叶斯推断新方法，采用神经网络作为代理模型。本文介绍sbijax，一个基于JAX实现的Python包，提供一系列前沿神经仿真推断方法的用户友好接口。sbijax支持快速构建SBI估计器，仅需少量代码即可计算和可视化后验分布。此外，该包还包含传统近似贝叶斯计算功能、模型诊断工具及自动摘要统计量估计。由于完全基于JAX，sbijax具有极高的计算效率，支持神经网络的快速训练，并可在CPU和GPU上自动并行执行。
原文摘要 · Abstract (English)
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.
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