马尔可夫逻辑网络

马尔可夫逻辑网络（Markov logic network (MLN))是由一阶逻辑公式及其对应的权值组成的二元组集合。马尔可夫逻辑网络的基本思想是将一阶逻辑的限制放松，即一个事件违反公式越多，其发生概率越小，但未必为0。

马尔可夫逻辑网络（MLNs）提供了一种紧凑的语言来描述超大规模的马尔可夫网络，并能灵活地将各类领域知识模块化地整合其中。从一阶逻辑的角度看，MLNs具备可靠处理不确定性、容忍不完善与矛盾知识的能力，从而降低了系统脆弱性。统计关系学习中的诸多重要任务——如集体分类、链接预测、基于链接的聚类、社交网络建模及对象识别——均可自然转化为MLN学习与推理的具体实例。

 參考文獻 



 外部連結 
 University of Washington Statistical Relational Learning group
 Alchemy 2.0: Markov logic networks in C++ 
 pracmln: Markov logic networks in Python 
 ProbCog: Markov logic networks in Python and Java that can use its own inference engine or Alchemy's
 markov thebeast: Markov logic networks in Java
 RockIt: Markov logic networks in Java (with web interface/REST API) 
 Tuffy: A Learning and Inference Engine with strong RDBMs-based optimization for scalability 
 Felix: A successor to Tuffy, with prebuilt submodules to speed up common subtasks 
 Factorie: Scala based probabilistic inference language, with prebuilt submodules for natural language processing etc 
 Figaro: Scala based MLN language 
 LoMRF: Logical Markov Random Fields, an open-source implementation of Markov Logic Networks in Scala 

Category:贝叶斯统计
Category:馬可夫網絡