论文标题
顺序神经评分估计：基于条件分数扩散模型的无似然推理
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
论文作者
论文摘要
我们引入了顺序神经后验评分估计（SNPSE），这是一种基于基于模拟器模型的贝叶斯推断的基于得分的方法。我们的方法受到生成建模中基于得分的方法的显着成功的启发，利用有条件的基于得分的扩散模型从兴趣的后验分布中生成样本。使用目标函数训练该模型，该目标函数直接估计后部的得分。我们将模型嵌入了一个顺序训练程序中，该程序在观察兴趣时使用后验的当前近似来指导模拟，从而降低了模拟成本。我们还介绍了几种替代顺序方法，并讨论它们的相对优点。然后，我们在几个数值示例上验证了我们的方法及其摊销，非顺序的变体，与现有的最新方法（例如顺序神经后验估计（SNPE））相比表现出可比或优越的性能。
We introduce Sequential Neural Posterior Score Estimation (SNPSE), a score-based method for Bayesian inference in simulator-based models. Our method, inspired by the remarkable success of score-based methods in generative modelling, leverages conditional score-based diffusion models to generate samples from the posterior distribution of interest. The model is trained using an objective function which directly estimates the score of the posterior. We embed the model into a sequential training procedure, which guides simulations using the current approximation of the posterior at the observation of interest, thereby reducing the simulation cost. We also introduce several alternative sequential approaches, and discuss their relative merits. We then validate our method, as well as its amortised, non-sequential, variant on several numerical examples, demonstrating comparable or superior performance to existing state-of-the-art methods such as Sequential Neural Posterior Estimation (SNPE).
