Multistage Monte Carlo Method for Solving Influence Diagrams Using Local Computation
提出一种多阶段蒙特卡洛模拟方法，通过局部计算仅对每个决策节点的一小部分机会变量采样，解决传统全局方法在变量多时样本量过大的问题，适用于影响图模型。
The main goal of this paper is to describe a new multistage Monte Carlo (MMC) simulation method for solving influence diagrams using local computation. Global methods have been proposed by others that sample from the joint probability distribution of all the variables in the influence diagram. However, for influence diagrams having many variables, the state space of all variables grows exponentially, and the sample sizes required for good estimates may be too large to be practical. In this paper, we develop a MMC method, which samples only a small set of chance variables for each decision node in the influence diagram. MMC is akin to methods developed for exact solution of influence diagrams in that we limit the number of chance variables sampled at any time. Because influence diagrams model each chance variable with a conditional probability distribution, the MMC method lends itself well to influence diagram representations.