论文标题
循环网络的信念传播
Belief propagation for networks with loops
论文作者
论文摘要
信仰传播是一种广泛使用的消息传递方法，用于解决诸如流行病模型，旋转模型和贝叶斯图形模型等网络上的概率模型的解决方案，但它遭受了严重的缺点，即在包含短循环的网络的常见情况下，它的工作差。在这里，我们提供了解决这个长期存在的问题的解决方案，该方法得出了一种信念传播方法，该方法允许在循环较短的系统中快速计算概率分布，可能具有高密度，并为熵和分区函数提供表达式，这是众所周知的很难计算的数量。以ISING模型为例，我们表明我们的方法在真实和合成网络上都能取得出色的结果，从而在标准消息传递方法上显着改善。我们还讨论了我们方法对其他各种问题的潜在应用。
Belief propagation is a widely used message passing method for the solution of probabilistic models on networks such as epidemic models, spin models, and Bayesian graphical models, but it suffers from the serious shortcoming that it works poorly in the common case of networks that contain short loops. Here we provide a solution to this long-standing problem, deriving a belief propagation method that allows for fast calculation of probability distributions in systems with short loops, potentially with high density, as well as giving expressions for the entropy and partition function, which are notoriously difficult quantities to compute. Using the Ising model as an example, we show that our approach gives excellent results on both real and synthetic networks, improving significantly on standard message passing methods. We also discuss potential applications of our method to a variety of other problems.
