ProbLog2: From probabilistic programming to statistical relational learning

ProbLog2: From probabilistic programming to statistical relational learning
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ProbLog2：从概率编程到统计关系学习

DOI：
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发表时间：
2012
期刊：
Neural Information Processing Systems
影响因子：
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通讯作者：
L. D. Raedt
L. D. Raedt
中科院分区：
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文献类型：
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作者：
Joris Renkens;D. Shterionov;Guy Van den Broeck;Jonas Vlasselaer;Daan Fierens;Wannes Meert;Gerda Janssens;L. D. Raedt

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Problog是一种基于Prolog的概率编程语言。称为ProBlog2的新的Problog系统可以解决概率图形模型（PGM）和统计关系学习（SRL）社区的典型推理和学习任务。 ProBlog2背后的主要机制是将给定程序转换为加权布尔公式。我们认为，这种转换方法也可以通过某些限制来应用于其他概率的编程语言，例如教堂和菲加罗。
ProbLog is a probabilistic programming language based on Prolog. The new ProbLog system called ProbLog2 can solve a range of inference and learning tasks typical for the Probabilistic Graphical Models (PGM) and Statistical Rela- tional Learning (SRL) communities. The main mechanism behind ProbLog2 is a conversion of the given program to a weighted Boolean formula. We argue that this conversion approach can also be applied with certain restrictions to other probabilistic programming languages such as Church and Figaro.