Distributional logic programming for Bayesian knowledge representation

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• 作者：Nicos Angelopoulos^a ; ^b ; ^{nicos.angelopoulos@sanger.ac.uk} ; James Cussens^c
• 关键词：Probabilistic logic programming ; Bayesian inference ; Bayesian networks ; Classification and regression trees ; Knowledge representation ; Logic programming
• 刊名：International Journal of Approximate Reasoning
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：80
• 期：Complete
• 页码：52-66
• 全文大小：528 K
• 卷排序：80

文摘

Knowledge representation for Bayesian machine learning. Probabilistic logic programming for modelling prior over model structure. Implementation of a system for likelihood based learning. Effect of prior information to proposal model structures. Proposal free MCMC simulations.