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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021-04-23
摘要
Belief propagation is a widely used message passing method for the solution of probabilistic models on 網絡s such as 流行病學c models, spin models, and 貝氏 graphical models, but it suffers from the serious shortcoming that it works poorly in the common case of 網絡s 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 熵 and partition function, which are notoriously difficult quantities to compute. Using the Ising模型 as an example, we show that our approach gives excellent results on both real and synthetic 網絡s, improving substantially on standard message passing methods. We
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