论文标题
部分可观测时空混沌系统的无模型预测
LDPC codes: comparing cluster graphs to factor graphs
论文作者
论文摘要
我们提出了LDPC代码的群集和因子图表示之间的比较研究。在概率图形模型中，群集图在推理过程中保留了随机变量之间的有用依赖性，这在计算成本，收敛速度和边际概率的准确性方面是有利的。这项研究在LDPC代码的背景下研究了这些好处，并表明群集图表示优于传统因子图表示。
We present a comparison study between a cluster and factor graph representation of LDPC codes. In probabilistic graphical models, cluster graphs retain useful dependence between random variables during inference, which are advantageous in terms of computational cost, convergence speed, and accuracy of marginal probabilities. This study investigates these benefits in the context of LDPC codes and shows that a cluster graph representation outperforms the traditional factor graph representation.
