Normals with unknown variance (NUV) can represent many useful priors and blend well with Gaussian models and message passing algorithms. NUV representations of sparsifying priors have long been known, and NUV representations of binary (and M-level) priors have been proposed very recently. In this document, we propose NUV representations of half-space constraints and box constraints, which allows to add such constraints to any linear Gaussian model with any of the previously known NUV priors without affecting the computational tractability.

翻译：具有未知差异的常态( NUV) 可以代表许多有用的前科, 并与Gaussian 模型和传递信息算法混为一谈。 NUV 的填充前科的表示方式早已为人所知, NUV 的二进制( 和 M 级) 表示方式最近才提出。 在本文件中, 我们提议 NUV 的半空限制和框限制表示方式, 从而可以在不影响计算可移动性的情况下, 将这种限制添加到任何已知的 NUV 前科线性模型中。