On the Efficient Execution of ProbLog Programs
On the Efficient Execution of ProbLog Programs
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论ProbLog程序的高效执行
DOI：
10.1007/978-3-540-89982-2_22
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
L. D. Raedt
中科院分区：
文献类型：
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作者：
Angelika Kimmig;V. S. Costa;Ricardo Rocha;Bart Demoen;L. D. Raedt
在过去的几年里，人们对概率逻辑学习或统计关系学习领域的兴趣激增。在这一奋进中，许多概率逻辑被开发出来。ProbLog是Prolog最近的一个概率扩展，其动机是挖掘大型生物网络。在ProbLog中，事实可以用它们属于随机抽样程序的相互独立的概率来标记。可以向ProbLog程序提出不同类型的查询。我们介绍的算法，使这些查询的有效执行，讨论他们的实施上的YAP-Prolog系统，并评估其性能的背景下，大型网络的生物实体。
The past few years have seen a surge of interest in the field of probabilistic logic learning or statistical relational learning. In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networks. In ProbLog, facts can be labeled with mutually independent probabilities that they belong to a randomly sampled program. Different kinds of queries can be posed to ProbLog programs. We introduce algorithms that allow the efficient execution of these queries, discuss their implementation on top of the YAP-Prolog system, and evaluate their performance in the context of large networks of biological entities.
通过图形模型进行正则化的集成卡尔曼滤波
DOI：
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发表时间：
2013
期刊：
影响因子：
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作者：
Ueno;G.
通讯作者：
G.