Driven by steady progress in generative modeling, simulation-based inference
(SBI) has enabled inference over stochastic simulators. However, recent work
has demonstrated that model misspecification can harm SBI's reliability. This
work introduces robust posterior estimation (ROPE), a framework that overcomes
model misspecification with a small real-world calibration set of ground truth
parameter measurements. We formalize the misspecification gap as the solution
of an optimal transport problem between learned representations of real-world
and simulated observations. Assuming the prior distribution over the parameters
of interest is known and well-specified, our method offers a controllable
balance between calibrated uncertainty and informative inference under all
possible misspecifications of the simulator. Our empirical results on four
synthetic tasks and two real-world problems demonstrate that ROPE outperforms
baselines and consistently returns informative and calibrated credible
intervals.

通过引入鲁棒后验估计 (ROPE)，克服了模型错误配置，ROPE 在真实世界校准集的基础上提供了具有可控平衡的校准不确定性和信息丰富的推断，通过解决真实观测和模拟观测之间的最优运输问题确保了模拟基于推理的可靠性。