优化重加权信念传播用于分布式似然融合问题
Optimizing Reweighted Belief Propagation for Distributed Likelihood Fusion Problems
AI总结：
本文研究了重加权信念传播在分布式似然融合问题中的应用，通过线性化BP方法推导收敛条件并优化收敛速度，相比标准信念共识方法具有更快的收敛性能。
摘要：
信念传播（BP）是一种强大的工具，用于解决分布式推断问题，尽管其受相应因子图中短环的限制。这些环可能导致错误的解决方案或振荡行为。只有在某些类型的问题中，收敛性质才被理解。我们通过研究重加权BP在分布式似然融合问题中的应用，扩展了这一知识。这些问题是通过可能较短的环中的等式约束特征。通过BP的线性化形式，我们能够对某些类型的图分析推导收敛条件，并优化收敛速度。我们与标准信念共识进行了比较，观察到显著更快的收敛。
英文摘要：
Belief propagation (BP) is a powerful tool to solve distributed inference problems, though it is limited by short cycles in the corresponding factor graph. Such cycles may lead to incorrect solutions or oscillatory behavior. Only for certain types of problems are convergence properties understood. We extend this knowledge by investigating the use of reweighted BP for distributed likelihood fusion problems, which are characterized by equality constraints along possibly short cycles. Through a linear formulation of BP, we are able to analytically derive convergence conditions for certain types of graphs and optimize the convergence speed. We compare with standard belief consensus and observe significantly faster convergence.
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