论文标题
FSPN：一类新的概率图形模型
FSPN: A New Class of Probabilistic Graphical Model
论文作者
论文摘要
我们介绍了一个新的概率图形模型（PGMS）的新类别的分解总和分型产品网络（FSPN）。 FSPN旨在从估计准确性和推理效率方面克服现有PGM的缺点。具体而言，贝叶斯网络（BNS）在存在高度相关变量的情况下，树结构性和产品网络（SPN）的推理速度和性能较低。 FSPN通过根据其依赖性自适应对变量的联合分布进行自适应建模，从而吸收其优势，从而可以同时实现两个理想的目标：高估计准确性和快速推理速度。我们提出了有效的概率推断和结构学习算法的FSPN，以及理论分析和广泛的评估证据。我们对合成和基准数据集的实验结果表明，FSPN优于其他PGM。
We introduce factorize sum split product networks (FSPNs), a new class of probabilistic graphical models (PGMs). FSPNs are designed to overcome the drawbacks of existing PGMs in terms of estimation accuracy and inference efficiency. Specifically, Bayesian networks (BNs) have low inference speed and performance of tree structured sum product networks(SPNs) significantly degrades in presence of highly correlated variables. FSPNs absorb their advantages by adaptively modeling the joint distribution of variables according to their dependence degree, so that one can simultaneously attain the two desirable goals: high estimation accuracy and fast inference speed. We present efficient probability inference and structure learning algorithms for FSPNs, along with a theoretical analysis and extensive evaluation evidence. Our experimental results on synthetic and benchmark datasets indicate the superiority of FSPN over other PGMs.
