Shwe M, Cooper G
Stanford University.
Comput Biomed Res. 1991 Oct;24(5):453-75. doi: 10.1016/0010-4809(91)90020-w.
We are developing a probabilistic reformulation of the Quick Medical Reference (QMR) system. Our current probabilistic model of the QMR knowledge base of internal medicine consists of a two-level, multiply connected, belief network. Because of the size and connectivity of this belief network, most exact algorithms for calculating the posterior marginal probabilities of diseases are not applicable. In this paper, we analyze the convergence properties of an approximation algorithm, called likelihood-weighting simulation, on the QMR-DT belief network. Specifically, on two difficult diagnostic cases, we examine the effects of Markov blanket scoring, importance sampling, and self-importance sampling, demonstrating that the Markov blanket scoring and self-importance sampling significantly improve the convergence of the simulation on our model.
我们正在开发一种快速医学参考（QMR）系统的概率性重新表述。我们当前关于内科QMR知识库的概率模型由一个两级、多重连接的信念网络组成。由于这个信念网络的规模和连接性，大多数用于计算疾病后验边缘概率的精确算法都不适用。在本文中，我们分析了一种称为似然加权模拟的近似算法在QMR-DT信念网络上的收敛特性。具体而言，在两个困难的诊断案例上，我们研究了马尔可夫毯评分、重要性采样和自重要性采样的效果，证明了马尔可夫毯评分和自重要性采样显著提高了我们模型模拟的收敛性。
