基于广义关联的单语概率规划

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摘要翻译：
概率编程语言和建模工具包是建立和重用随机模型和推理过程的两种模块化方法。结合两者的优点，我们用同一通用语言将模型和推理表示为广义关联子。我们使用该语言的现有设施，如丰富的库、优化的编译器和类型，来开发简洁、声明性和逼真的模型，在精确和近似的推理上具有竞争性能。特别是，广泛的模型可以用记忆来表达。由于模型的确定性部分以全速运行，自定义推理过程很容易合并，而且推理过程可以在没有解释开销的情况下进行推理。在此框架内，我们提出了一种新的、通用的具有前瞻性的重要性抽样算法。
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英文标题：
《Monolingual Probabilistic Programming Using Generalized Coroutines》
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作者：
Oleg Kiselyov, Chung-chieh Shan
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Programming Languages        程序设计语言
分类描述：Covers programming language semantics, language features, programming approaches (such as object-oriented programming, functional programming, logic programming). Also includes material on compilers oriented towards programming languages; other material on compilers may be more appropriate in Architecture (AR). Roughly includes material in ACM Subject Classes D.1 and D.3.
涵盖程序设计语言语义，语言特性，程序设计方法（如面向对象程序设计，函数式程序设计，逻辑程序设计）。还包括面向编程语言的编译器的材料；关于编译器的其他材料可能在体系结构(AR)中更合适。大致包括ACM主题课程D.1和D.3中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Probabilistic programming languages and modeling toolkits are two modular ways to build and reuse stochastic models and inference procedures. Combining strengths of both, we express models and inference as generalized coroutines in the same general-purpose language. We use existing facilities of the language, such as rich libraries, optimizing compilers, and types, to develop concise, declarative, and realistic models with competitive performance on exact and approximate inference. In particular, a wide range of models can be expressed using memoization. Because deterministic parts of models run at full speed, custom inference procedures are trivial to incorporate, and inference procedures can reason about themselves without interpretive overhead. Within this framework, we introduce a new, general algorithm for importance sampling with look-ahead.
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PDF链接：
https://arxiv.org/pdf/1205.2636

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关键词：Intelligence Presentation Architecture Programming Generalized Programming 广义 自定义 模型 前瞻性