Inferential models (IMs) represent a novel possibilistic approach for achieving provably valid statistical inference. This paper introduces a general framework for fusing independent IMs in a "black-box" manner, requiring no knowledge of the original IMs construction details. The underlying logic of this framework mirrors that of the IMs approach. First, a fusing function for the initial IMs' possibility contours is selected. Given the possible lack of guarantee regarding the calibration of this function for valid inferences, a "validification" step is performed. Subsequently, a straightforward normalization step is executed to ensure that the final output conforms to a possibility contour.

翻译：推断模型（IMs）代表了一种新颖的可能性方法，旨在实现可证明有效的统计推断。本文提出了一种在“黑箱”方式下融合独立推断模型的通用框架，无需了解原始推断模型的构建细节。该框架的基本逻辑与推断模型方法一致。首先，为初始推断模型的可能性轮廓选择融合函数。鉴于该函数可能无法保证校准以实现有效推断，会执行一个“有效性验证”步骤。随后，进行简单的归一化步骤，以确保最终输出符合可能性轮廓。