论文标题
$α$信念传播大约推断
$α$ Belief Propagation for Approximate Inference
论文作者
论文摘要
信念传播（BP）算法是一种广泛使用的消息通话方法，用于在图形模型中推断。 BP无环图在线性时间内收敛。但是，对于具有循环的图表，BP的性能是不确定的，并且对解决方案的理解是有限的。为了更好地了解一般图中的BP，我们得出了一种可解释的信念传播算法，该算法是通过最小化局部$α$ divergence的最小化而动机的。我们将此算法称为$α$信念传播（$α$ -bp）。事实证明，$α$ -BP概括了标准bp。此外，这项工作研究了$α$ -bp的收敛性能。我们证明并提供$α$ -BP的收敛条件。随机图上的实验模拟验证了我们的理论结果。还显示了$α$ -BP在实际问题上的应用。
Belief propagation (BP) algorithm is a widely used message-passing method for inference in graphical models. BP on loop-free graphs converges in linear time. But for graphs with loops, BP's performance is uncertain, and the understanding of its solution is limited. To gain a better understanding of BP in general graphs, we derive an interpretable belief propagation algorithm that is motivated by minimization of a localized $α$-divergence. We term this algorithm as $α$ belief propagation ($α$-BP). It turns out that $α$-BP generalizes standard BP. In addition, this work studies the convergence properties of $α$-BP. We prove and offer the convergence conditions for $α$-BP. Experimental simulations on random graphs validate our theoretical results. The application of $α$-BP to practical problems is also demonstrated.
