网格计算
Trellis Computations
摘要：
对于某类函数，可以在网格或子网格中计算函数值的分布。从BCJR算法已知的前向/后向递归被推广到计算这些分布的矩。类似于符号概率，通过在网格中的某个深度处引入约束，我们获得符号矩。这些矩是有效实现[2]中的区分置信传播算法所必需的，并且还可以用于计算网格中的条件熵。矩计算算法具有与BCJR算法相同的渐近复杂度。它适用于任何交换半环，从而实际上提供了维特比算法的推广。
英文摘要：
For a certain class of functions, the distribution of the function values can be calculated in the trellis or a sub-trellis. The forward/backward recursion known from the BCJR algorithm is generalized to compute the moments of these distributions. In analogy to the symbol probabilities, by introducing a constraint at a certain depth in the trellis we obtain symbol moments. These moments are required for an efficient implementation of the discriminated belief propagation algorithm in [2], and can furthermore be utilized to compute conditional entropies in the trellis. The moment computation algorithm has the same asymptotic complexity as the BCJR algorithm. It is applicable to any commutative semi-ring, thus actually providing a generalization of the Viterbi algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
