Online Bayesian inference for the parameters of PRISM programs

作     者：Cussens, James 

作者机构：Univ York Dept Comp Sci York YO10 5GE N Yorkshire England Univ York York Ctr Complex Syst Anal York YO10 5GE N Yorkshire England 

出 版 物：《MACHINE LEARNING》 (机器学习)

年 卷 期：2012年第89卷第3期

页      面：279-297页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Inductive logic programming Bayesian statistics Statistical relational learning PRISM Mixture models Missing data 

摘      要：This paper presents a method for approximating posterior distributions over the parameters of a given PRISM program. A sequential approach is taken where the distribution is updated one datapoint at a time. This makes it applicable to online learning situations where data arrives over time. The method is applicable whenever the prior is a mixture of products of Dirichlet distributions. In this case the true posterior will be a mixture of very many such products. An approximation is effected by merging products of Dirichlet distributions. An analysis of the quality of the approximation is presented. Due to the heavy computational burden of this approach, the method has been implemented in the Mercury logic programming language. Initial results using a hidden Markov model and a probabilistic graph are presented.