通过选择性条件反射重构推理问题
Reformulating Inference Problems Through Selective Conditioning
摘要：
我们描述了我们如何选择性地重新制定的信念网络的一部分，构成困难的解决方案与随机模拟算法。采用选择性条件作用方法，根据节点对随机模拟易处理性的贡献，将信度网络中的特定节点作为分解目标。我们回顾了BNRAS算法-概率推理的随机近似算法的前期工作。我们展示了如何选择性条件反射可以采用重新制定一个单一的BNRAS问题到多个易处理的BNRAS模拟问题。我们将讨论如何使用另一种模拟算法-逻辑采样-来解决推理问题的一个组成部分，该推理问题提供了一种将各个子问题的解决方案编织成最终结果的方法。最后，我们分析了权衡与选择性条件反射的方法来重新制定的计算子任务。
英文摘要：
We describe how we selectively reformulate portions of a belief network that pose difficulties for solution with a stochastic-simulation algorithm. With employ the selective conditioning approach to target specific nodes in a belief network for decomposition, based on the contribution the nodes make to the tractability of stochastic simulation. We review previous work on BNRAS algorithms- randomized approximation algorithms for probabilistic inference. We show how selective conditioning can be employed to reformulate a single BNRAS problem into multiple tractable BNRAS simulation problems. We discuss how we can use another simulation algorithm-logic sampling-to solve a component of the inference problem that provides a means for knitting the solutions of individual subproblems into a final result. Finally, we analyze tradeoffs among the computational subtasks associated with the selective conditioning approach to reformulation.
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