对称贝叶斯决策问题的惰性评价
Lazy Evaluation of Symmetric Bayesian Decision Problems
摘要：
解决对称贝叶斯决策问题是一个计算密集型的任务，无论使用的算法。在本文中，我们提出了一种方法来提高效率的算法解决贝叶斯决策问题。该方法是基于惰性评估的原则-最近被证明可以提高贝叶斯网络中的推理效率的原则。其基本思想是保持分解的潜力，并推迟计算尽可能长的时间。通过实例强调了基于惰性评估的方法所获得的效率改进。最后，将基于惰性评估的方法与基于Hugin和基于评估的系统架构在解决对称贝叶斯决策问题上进行了比较。
英文摘要：
Solving symmetric Bayesian decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the efficiency of algorithms for solving Bayesian decision problems. The method is based on the principle of lazy evaluation - a principle recently shown to improve the efficiency of inference in Bayesian networks. The basic idea is to maintain decompositions of potentials and to postpone computations for as long as possible. The efficiency improvements obtained with the lazy evaluation based method is emphasized through examples. Finally, the lazy evaluation based method is compared with the hugin and valuation-based systems architectures for solving symmetric Bayesian decision problems.
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