提出低成本仿真推断方法，减少复杂模型参数估计的计算开销。
Cost-aware simulation-based inference
- 结合拒绝采样与自归一化重要性采样，智能选择仿真次数。
- 在流行病学与通信工程模型中，显著降低整体推断成本。
- 适合需频繁仿真的科学与工程领域研究人员使用。
仿真推断（SBI）是科学与工程中估算不可解析模型参数的首选框架。其主要挑战在于复杂模型生成数据的高计算成本，且该成本常随参数值变化。为此，我们提出「成本感知型SBI方法」，可显著降低现有基于采样的SBI方法（如神经SBI和近似贝叶斯计算）的计算成本。该方法通过结合拒绝采样与自归一化重要性采样，大幅减少昂贵仿真次数。我们在流行病学至通信工程等多个模型上进行了广泛验证，均取得显著的成本降低效果。
原文摘要 · Abstract (English)
Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering. A significant challenge in this context is the large computational cost of simulating data from complex models, and the fact that this cost often depends on parameter values. We therefore propose \textit{cost-aware SBI methods} which can significantly reduce the cost of existing sampling-based SBI methods, such as neural SBI and approximate Bayesian computation. This is achieved through a combination of rejection and self-normalised importance sampling, which significantly reduces the number of expensive simulations needed. Our approach is studied extensively on models from epidemiology to telecommunications engineering, where we obtain significant reductions in the overall cost of inference.
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