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)，这是一种用于概率编程语言(PPLs)中自动近似推断的新方法。DMVI利用扩散模型作为真实后验分布的变分近似，通过推导贝叶斯建模中边际似然目标的一个新边界来实现。DMVI易于实现，允许在PPLs中实现无干扰推断，避免了如使用归一化流的变分推断等方法的缺陷，并且不对底层神经网络模型施加任何约束。我们在一组常见贝叶斯模型上评估了DMVI，结果表明其后验推断通常比PPLs中使用的当代方法更准确，同时具有相似的计算成本且需要更少的手动调谐。