Significance testing aims to determine whether a proposition about the
population distribution is the truth or not given observations. However,
traditional significance testing often needs to derive the distribution of the
testing statistic, failing to deal with complex nonlinear relationships. In
this paper, we propose to conduct Full Bayesian Significance Testing for neural
networks, called \textit{n}FBST, to overcome the limitation in relationship
characterization of traditional approaches. A Bayesian neural network is
utilized to fit the nonlinear and multi-dimensional relationships with small
errors and avoid hard theoretical derivation by computing the evidence value.
Besides, \textit{n}FBST can test not only global significance but also local
and instance-wise significance, which previous testing methods don't focus on.
Moreover, \textit{n}FBST is a general framework that can be extended based on
the measures selected, such as Grad-\textit{n}FBST, LRP-\textit{n}FBST,
DeepLIFT-\textit{n}FBST, LIME-\textit{n}FBST. A range of experiments on both
simulated and real data are conducted to show the advantages of our method.

提出了一种全贝叶斯显著性测试方法（nFBST），采用贝叶斯神经网络来拟合非线性和多维关系，并通过计算证据值避免了传统方法中的理论派生难题，可以测试全局、局部和实例显著性，并且是一个可根据所选择的度量进行扩展的通用框架。