2022/10/04 by Kyoungchul Kong, Kong, Kyoungchul, K. Matchev +5 · 2 citations
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Physical sciences #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.2210.01680
openalex publication_date 2022/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential φ. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.