A New Inference Framework for Dependency Networks

作者：Chen Feng^*; Cheng Qiang; Liu Hong; Xu Wenli; Wang Song

来源：Communications in Statistics - Theory and Methods, 2013, 42(1): 56-75.

DOI：10.1080/03610926.2011.573157

• Approximate inference
• Dependency network
• Full conditional distribution

Dependency networks (DNs) have been receiving more attention recently because their structures and parameters can be easily learned from data. The full conditional distributions (FCDs) are known conditions of DNs. Gibbs sampling is currently the most popular inference method on DNs. However, sampling methods converge slowly and it can be hard to diagnose their convergence. In this article, we introduce a set of linear equations to describe the relations between joint probability distributions (JPDs) and FCDs. These equations provide a novel perspective to understand reasoning on DNs. Based on these linear equations, we develop both exact and approximate algorithms for inference on DNs. Experiments show that the proposed approximate algorithms can produce effective results by maintaining low computational complexity.

• 出版日期2013-1-1
• 单位清华大学