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.
Gladstone Institute of Data Science and Biotechnology（格拉德斯通数据科学与生物技术研究所）；Department of Biomedical Data Science, Stanford University（斯坦福大学生物医学数据科学系）；Department of Statistics and Data Science, Carnegie Mellon University（卡内基梅隆大学统计与数据科学系）；Machine Learning Department, Carnegie Mellon University（卡内基梅隆大学机器学习系）
Unified estimation framework for unnormalized models with statistical efficiency
Harvard University（哈佛大学）；Tokyo Institute of Technology（东京工业大学）；RIKEN AIP（理化学研究所人工智能研究中心）；Future University Hakodate（函馆未来大学）；The University of Tokyo（东京大学）；RIKEN Center for Brain Science（理化学研究所脑科学中心）
