论文标题
贝叶斯的绘制方法
A Bayesian Approach To Graph Partitioning
论文作者
论文摘要
给出了一种基于贝叶斯推断的新算法，用于学习基于高斯工艺（GP）的本地图电导，该算法使用高级MCMC收敛想法来创建可扩展且快速的算法，以收敛到固定分布，以在穿越间接的加权图时提供用于学习电导率的巴哈维尔。第一个度量嵌入用于表示图的顶点。然后，针对训练点计算均匀的诱导电导。最后，在学习步骤中，使用高斯过程来近似均匀的诱导电导。 MCMC用于测量估计的超参数的不确定性。
A new algorithm based on bayesian inference for learning local graph conductance based on Gaussian Process(GP) is given that uses advanced MCMC convergence ideas to create a scalable and fast algorithm for convergence to stationary distribution which is provided to learn the bahavior of conductance when traversing the indirected weighted graph. First metric embedding is used to represent the vertices of the graph. Then, uniform induced conductance is calculated for training points. Finally, in the learning step, a gaussian process is used to approximate the uniform induced conductance. MCMC is used to measure uncertainty of estimated hyper-parameters.
