🤖 AI Summary
This study addresses the high computational cost and insufficient reliability guarantees of neural simulation-based inference by proposing a hybrid inference framework incorporating semiparametric formulations. By introducing two mixture strategies, including latent classes, the method effectively balances inference sensitivity with computational efficiency while preserving the statistical reliability of parametric models. Experimental results demonstrate that the proposed approach significantly reduces computational overhead at the expense of only marginal sensitivity loss. It supports both offline analysis and future trigger-level real-time applications, providing a theoretically grounded and practically valuable solution for efficient and robust statistical inference.
📝 Abstract
We develop two hybrid techniques that approach the performance of neural simulation-based inference (NSBI) analyses while substantially reducing the computational cost of inference and preserving some or all of the reliability guarantees of parametric methods. The first approach is broadly applicable, while the second is tailored to a class of particle physics analyses that admit a semi-parametric NSBI formulation.
With only a modest compromise in raw sensitivity, these methods represent an important step toward computationally efficient NSBI in offline analyses and also open the door to the exploration of trigger-level applications in the future. Based on our comparison studies, we recommend the use of our first approach, Latent Categories, for robust and efficient inference.