When analyzing real-world data it is common to work with event ensembles,
which comprise sets of observations that collectively constrain the parameters
of an underlying model of interest. Such models often have a hierarchical
structure, where "local" parameters impact individual events and "global"
parameters influence the entire dataset. We introduce practical approaches for
optimal dataset-wide probabilistic inference in cases where the likelihood is
intractable, but simulations can be realized via forward modeling. We construct
neural estimators for the likelihood(-ratio) or posterior and show that
explicitly accounting for the model's hierarchical structure can lead to
tighter parameter constraints. We ground our discussion using case studies from
the physical sciences, focusing on examples from particle physics (particle
collider data) and astrophysics (strong gravitational lensing observations).

本文介绍了一种利用神经估计器进行概率推断的方法，针对具有层次结构的模型，从实际应用的角度出发，利用粒子碰撞数据和强引力透镜观测数据进行案例研究并得出相应的结论。