Robust Surrogate-Based Bayesian Inference via Sampling-Based Adaptive Active Learning (SALE)
基于采样的自适应主动学习:稳健的代理贝叶斯推断
Bayesian inference is difficult when likelihood evaluations are expensive and budgets are limited. We propose sampling-based adaptive active learning (SALE), a Gaussian-process (GP) framework for surrogate-based Bayesian inference. SALE uses the expected posterior (EP) induced by normalised GP sample paths as a common sequential-design measure: it defines a posterior-guided search region and weights uncertainty reduction (UR). A state-dependent rule allocates evaluations…