We introduce two synthetic likelihood methods for Simulation-Based Inference (SBI), to conduct either amortized or targeted inference from experimental observations when a high-fidelity simulator is available. Both methods learn a conditional energy-based model (EBM) of the likelihood using synthetic data generated by the simulator, conditioned on parameters drawn from a proposal distribution. The learned likelihood can then be combined with any prior to obtain a posterior estimate, from which samples can be drawn using MCMC. Our methods uniquely combine a flexible Energy-Based Model and the minimization of a KL loss: this is in contrast to other synthetic likelihood methods, which either rely on normalizing flows, or minimize score-based objectives; choices that come with known pitfalls. We demonstrate the properties of both methods on a range of synthetic datasets, and apply them to a neuroscience model of the pyloric network in the crab, where our method outperforms prior art for a fraction of the simulation budget.

翻译：我们提出了两种用于基于模拟的推理（SBI）的合成似然方法，用于在存在高保真模拟器时，从实验观测中进行分摊式或目标导向的推理。两种方法均利用模拟器生成的合成数据，学习条件能量基模型（EBM）作为似然函数，该模型以从提议分布中抽取的参数为条件。学得的似然函数可与任意先验结合以获取后验估计，随后通过马尔可夫链蒙特卡洛（MCMC）方法从中采样。我们的方法独特地结合了灵活的能量基模型与KL散度损失的最小化：这与其他合成似然方法形成对比——后者要么依赖归一化流，要么最小化评分目标函数；这些选择本身存在已知缺陷。我们在多个合成数据集上验证了两种方法的特性，并将其应用于螃蟹幽门网络的一个神经科学模型，在该模型中，我们的方法以更少的模拟预算超越了现有技术。