Decision Analysis by Augmented Probability Simulation
提出一种通用蒙特卡洛方法，通过构建人工分布并抽取样本，利用探索性数据分析工具近似识别期望效用最大的备选方案，适用于多种影响图。
We provide a generic Monte Carlo method to find the alternative of maximum expected utility in a decision analysis. We define an artificial distribution on the product space of alternatives and states, and show that the optimal alternative is the mode of the implied marginal distribution on the alternatives. After drawing a sample from the artificial distribution, we may use exploratory data analysis tools to approximately identify the optimal alternative. We illustrate our method for some important types of influence diagrams.