Across many domains of science, stochastic models are an essential tool to understand the mechanisms underlying empirically observed data. Models can be of different levels of detail and accuracy, with models of high-fidelity (i.e., high accuracy) to the phenomena under study being often preferable. However, inferring parameters of high-fidelity models via simulation-based inference is challenging, especially when the simulator is computationally expensive. We introduce a multifidelity approach to neural posterior estimation that uses transfer learning to leverage inexpensive low-fidelity simulations to efficiently infer parameters of high-fidelity simulators. Our method applies the multifidelity scheme to both amortized and non-amortized neural posterior estimation. We further improve simulation efficiency by introducing a sequential variant that uses an acquisition function targeting the predictive uncertainty of the density estimator to adaptively select high-fidelity parameters. On established benchmark and neuroscience tasks, our approaches require up to two orders of magnitude fewer high-fidelity simulations than current methods, while showing comparable performance. Overall, our approaches open new opportunities to perform efficient Bayesian inference on computationally expensive simulators.

翻译：在众多科学领域中，随机模型是理解经验观测数据背后机制的重要工具。模型可具有不同详细程度与准确性，其中与研究对象高保真（即高精度）的模型通常更受青睐。然而，基于仿真推断方法对高保真模型进行参数推断颇具挑战，尤其当模拟器计算成本高昂时。我们提出一种基于迁移学习的多保真度神经后验估计方法，利用低成本的低保真仿真数据高效推断高保真模拟器参数。该方法将多保真度框架同时应用于摊销式与非摊销式神经后验估计。通过引入一种基于密度估计器预测不确定性的采集函数进行自适应高保真参数选取的序贯变体，我们进一步提升了仿真效率。在标准基准测试与神经科学任务中，我们的方法所需高保真仿真量比现有方法减少两个数量级，同时保持可比的性能表现。总体而言，本方法为计算代价高昂的模拟器实现高效贝叶斯推断开辟了新途径。