Statistical relational AI and probabilistic logic programming have so far
mostly focused on discrete probabilistic models. The reasons for this is that
one needs to provide constructs to succinctly model the independencies in such
models, and also provide efficient inference.
Three types of independencies are important to represent and exploit for
scalable inference in hybrid models: conditional independencies elegantly
modeled in Bayesian networks, context-specific independencies naturally
represented by logical rules, and independencies amongst attributes of related
objects in relational models succinctly expressed by combining rules.
This paper introduces a hybrid probabilistic logic programming language, DC#,
which integrates distributional clauses' syntax and semantics principles of
Bayesian logic programs. It represents the three types of independencies
qualitatively. More importantly, we also introduce the scalable inference
algorithm FO-CS-LW for DC#. FO-CS-LW is a first-order extension of the
context-specific likelihood weighting algorithm (CS-LW), a novel sampling
method that exploits conditional independencies and context-specific
independencies in ground models. The FO-CS-LW algorithm upgrades CS-LW with
unification and combining rules to the first-order case.

本文介绍了一种混合概率逻辑编程语言 DC#，它将分布式子句的语法与贝叶斯逻辑程序的语义原则集成到一起。该语言以质量为基础表示了三种独立性，并引入了可扩展的推理算法 FO-CS-LW，该算法是 CS-LW 的一阶扩展，结合了一阶统一和规则组合。

混合概率逻辑程序中的一阶上下文特定似然加权

First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs

An efficient algorithm is developed that identifies all independencies
implied by the topology of a Bayesian network. Its correctness and maximality
stems from the soundness and completeness of d-separation with respect to
probability theory. The algorithm runs in time O (l E l) where E is the number
of edges in the network.

本文提出了一种高效算法，能够根据贝叶斯网络的拓扑结构确定所有的相关性，算法的正确性和最大性源于基于概率理论的 d - 分离的完备性和正确性。该算法的时间复杂度为 O (lE l)，E 为网络中边的数量。