BayesFlow 2: Python中多后端的近似贝叶斯推断
BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python
AI总结：
本文介绍BayesFlow 2.0，通过训练神经网络实现快速推断，支持多种深度学习后端和生成网络，提供定制化接口和超参数优化等功能，展示其在动态系统参数估计中的应用潜力。
摘要：
现代贝叶斯推断涉及多种计算方法用于估计、验证和从概率模型中得出结论，作为原则性工作流的一部分。许多贝叶斯方法的一个共同特征是速度较慢，这在拟合复杂模型到大数据集时常常成为瓶颈。近似贝叶斯推断（ABI）提供了解决贝叶斯计算挑战的途径。ABI通过在模型模拟上训练神经网络，使用户能够快速推断任何模型隐含的量，如点估计、似然或完整后验分布。本文介绍了Python库BayesFlow，版本2.0，用于通用的近似贝叶斯推断。除了直接后验、似然和比率估计外，该软件还支持多种流行的深度学习后端，丰富的生成网络用于采样和密度估计，完整的自定义和高级接口，以及新的超参数优化、设计优化和层次模型能力。通过动态系统参数估计案例研究和与类似软件的比较，我们展示了我们的流线型、用户友好的工作流具有广泛采用的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
