查询DAG：一种实现信念网络推理的实用范式
Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference
摘要：
我们描述了一个新的范例，用于实现推理的信念网络，它包括两个步骤：（1）编译一个信念网络到一个算术表达式称为查询DAG（Q-DAG）;（2）回答查询使用一个简单的评估算法。Q-DAG的每个节点代表一个数值运算、一个数字或一个符号作为证据。Q-DAG的每个叶节点代表网络查询的答案，即某些感兴趣事件的概率。看来，Q-DAG可以使用任何标准算法在信念网络中进行精确推理（我们展示了如何使用聚类和条件算法生成它们）。Q-DAG生成算法的时间和空间复杂度并不比它所基于的推理算法的时间复杂度差。Q-DAG求值算法的复杂度与Q-DAG的大小成线性关系，并且这样的推断相当于它所表示的算术表达式的标准求值。Q-DAG的预期价值在于减少在线、真实世界应用中利用信念网络所需的软件和硬件资源。由于Q-DAG评估算法的简单性，所提出的框架也有利于在不同的软件和硬件平台上开发在线推理。有趣的是，Q-DAG被发现用于其他目的：减少Q-DAG的简单技术往往会取代相对复杂的信念网络推理优化技术，如网络修剪和计算缓存。
英文摘要：
We describe a new paradigm for implementing inference in belief networks, which consists of two steps: (1) compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG); and (2) answering queries using a simple evaluation algorithm. Each node of a Q-DAG represents a numeric operation, a number, or a symbol for evidence. Each leaf node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. It appears that Q-DAGs can be generated using any of the standard algorithms for exact inference in belief networks (we show how they can be generated using clustering and conditioning algorithms). The time and space complexity of a Q-DAG generation algorithm is no worse than the time complexity of the inference algorithm on which it is based. The complexity of a Q-DAG evaluation algorithm is linear in the size of the Q-DAG, and such inference amounts to a standard evaluation of the arithmetic expression it represents. The intended value of Q-DAGs is in reducing the software and hardware resources required to utilize belief networks in on-line, real-world applications. The proposed framework also facilitates the development of on-line inference on different software and hardware platforms due to the simplicity of the Q-DAG evaluation algorithm. Interestingly enough, Q-DAGs were found to serve other purposes: simple techniques for reducing Q-DAGs tend to subsume relatively complex optimization techniques for belief-network inference, such as network-pruning and computation-caching.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
