Probabilistic programming languages are used for developing statistical models. They typically consist of two components: a specification of a stochastic process (the prior) and a specification of observations that restrict the probability space to a conditional subspace (the posterior). Use cases of such formalisms include the development of algorithms in machine learning and artificial intelligence. In this article, we establish a probabilistic-programming extension of Datalog that, on the one hand, allows for defining a rich family of statistical models, and on the other hand retains the fundamental properties of declarativity. Our proposed extension provides mechanisms to include common numerical probability functions; in particular, conclusions of rules may contain values drawn from such functions. The semantics of a program is a probability distribution over the possible outcomes of the input database with respect to the program. Observations are naturally incorporated by means of integrity constraints over the extensional and intensional relations. The resulting semantics is robust under different chases and invariant to rewritings that preserve logical equivalence.

中文翻译：

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使用 Datalog 进行声明性概率规划

概率编程语言用于开发统计模型。它们通常由两个部分组成：随机过程规范（先验）和将概率空间限制为条件子空间（后验）的观察规范。这种形式的用例包括机器学习和人工智能算法的开发。在本文中，我们建立了 Datalog 的概率编程扩展，一方面允许定义丰富的统计模型系列，另一方面保留声明性的基本属性。我们提出的扩展提供了包含常见数值概率函数的机制；特别是，规则的结论可能包含从这些函数中得出的值。程序的语义是输入数据库关于程序的可能结果的概率分布。观察通过对外延和内涵关系的完整性约束自然地结合起来。由此产生的语义在不同的追逐下是健壮的，并且对于保持逻辑等价的重写是不变的。