Inferential models have been proposed for valid and efficient prior-free probabilistic inference. As it gradually gained popularity, this theory is subject to further developments for practically challenging problems. This paper considers the many-normal-means problem with the means constrained to be in the neighborhood of each other, formally represented by a H\"older space. A new method, called partial conditioning, is proposed to generate valid and efficient marginal inference about the individual means. It is shown that the method outperforms both a fiducial-counterpart in terms of validity and a conservative-counterpart in terms of efficiency. We conclude the paper by remarking that a general theory of partial conditioning for inferential models deserves future development.

翻译：推理模型已被提出用于实现有效且高效的无先验概率推理。随着该理论逐渐普及，针对实际挑战性问题的新发展不断涌现。本文研究均值受邻域约束（形式化表述为赫尔德空间）的多正态均值问题，提出一种名为"部分条件化"的新方法，用于生成关于个体均值的有效且高效的边缘推断。研究表明，该方法在有效性上优于 fiducial 对应方法，在效率上优于保守对应方法。最后，本文指出发展推理模型的部分条件化一般理论值得未来深入探讨。