用神经网络加速贝叶斯推断，支持多后端快速建模。
BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python
- 用神经网络拟合模型模拟数据，实现快速贝叶斯推断。
- 支持后验、似然、比值估计，可处理复杂动态系统参数。
- 兼容多种深度学习框架，适合需要高效推断的研究者。
现代贝叶斯推断依赖多种计算方法来估计、验证并从概率模型中得出结论，形成系统化工作流程。许多贝叶斯方法的共同问题是速度慢，尤其在复杂模型与大规模数据下难以应用。摊销贝叶斯推断（ABI）通过在模型模拟数据上训练神经网络，使用户能快速获得任意模型相关量的推断结果，如点估计、似然值或完整的后验分布。本文介绍 Python 工具库 BayesFlow 2.0，支持通用型 ABI。除直接后验、似然和比值估计外，该软件还支持多个主流深度学习后端，提供丰富的生成网络用于采样与密度估计，具备完全可定制性及高级接口，并新增超参数优化、设计优化与分层建模功能。通过动力系统参数估计案例研究，结合与其他同类工具的对比，表明其简洁易用的工作流具有广泛采用潜力。
原文摘要 · Abstract (English)
Modern Bayesian inference involves a mixture of computational methods for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows. An overarching motif of many Bayesian methods is that they are relatively slow, which often becomes prohibitive when fitting complex models to large data sets. Amortized Bayesian inference (ABI) offers a path to solving the computational challenges of Bayes. ABI trains neural networks on model simulations, rewarding users with rapid inference of any model-implied quantity, such as point estimates, likelihoods, or full posterior distributions. In this work, we present the Python library BayesFlow, Version 2.0, for general-purpose ABI. Along with direct posterior, likelihood, and ratio estimation, the software includes support for multiple popular deep learning backends, a rich collection of generative networks for sampling and density estimation, complete customization and high-level interfaces, as well as new capabilities for hyperparameter optimization, design optimization, and hierarchical modeling. Using a case study on dynamical system parameter estimation, combined with comparisons to similar software, we show that our streamlined, user-friendly workflow has strong potential to support broad adoption.
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