We propose Diffusion Model Variational Inference (DMVI), a novel method for automated approximate inference in probabilistic programming languages (PPLs). DMVI utilizes diffusion models as variational approximations to the true posterior distribution by deriving a novel bound to the marginal likelihood objective used in Bayesian modelling. DMVI is easy to implement, allows hassle-free inference in PPLs without the drawbacks of, e.g., variational inference using normalizing flows, and does not make any constraints on the underlying neural network model. We evaluate DMVI on a set of common Bayesian models and show that its posterior inferences are in general more accurate than those of contemporary methods used in PPLs while having a similar computational cost and requiring less manual tuning.

翻译：我们提出扩散模型变分推断（DMVI），这是一种在概率编程语言（PPL）中实现自动化近似推断的新方法。DMVI通过推导贝叶斯建模中边际似然目标的新型边界，将扩散模型用作真实后验分布的变分近似。DMVI易于实现，可在PPL中实现无干扰推断，且无需应对如正规化流变分推断等方法的局限，同时对底层神经网络模型不做任何约束。我们在多个标准贝叶斯模型上对DMVI进行评估，结果表明：在计算成本与人工调参需求相似的情况下，其后验推断精度普遍优于PPL中现有方法。