Although theoretically compelling, Bayesian learning with modern machine learning models is computationally challenging since it requires approximating a high dimensional posterior distribution. In this work, we (i) introduce posteriors, an easily extensible PyTorch library hosting general-purpose implementations making Bayesian learning accessible and scalable to large data and parameter regimes; (ii) present a tempered framing of stochastic gradient Markov chain Monte Carlo, as implemented in posteriors, that transitions seamlessly into optimization and unveils a minor modification to deep ensembles to ensure they are asymptotically unbiased for the Bayesian posterior, and (iii) demonstrate and compare the utility of Bayesian approximations through experiments including an investigation into the cold posterior effect and applications with large language models.

通过引入易于扩展的PyTorch库posteriors，我们在理论上具有吸引力的贝叶斯学习与现代机器学习模型的计算挑战得以克服，从而使得贝叶斯学习在大规模数据和参数范围下变得可访问且可扩展；通过在posteriors实现中引入一种温度设定的随机梯度马尔可夫链蒙特卡洛方法，平稳过渡到优化，并揭示了深度集成的一种次要修改，以确保它们对贝叶斯后验具有渐进无偏性；通过包括对冷后验效应的研究和对大型语言模型的应用实验来展示和比较贝叶斯近似的实用性。

可扩展的贝叶斯学习与后验概率