Bryg D J
Department of Internal Medicine, University of Nevada, Reno, USA.
Med Decis Making. 1995 Oct-Dec;15(4):318-32. doi: 10.1177/0272989X9501500403.
This paper introduces an improved technique for modeling risk and decision problems that have continuous random variables and probabilistic dependence. Variables are modeled with mixtures of four-parameter random variables, called "continuous trees." Functions of random variables are calculated using gaussian quadrature in a manner called "Nevada simulation" (NumErical Integration of Variance And probabilistic Dependence Analyzer). This technique is compared with traditional decision-tree modeling in terms of analytic technique, solution-time complexity, and accuracy. Nevada simulation takes advantage of the probabilistic independence in a decision problem while allowing for probabilistic dependence to achieve polynomial computational-time complexity for many decision problems. It improves on the accuracy of traditional decision trees by employing larger approximations than traditional decision analysis. It improves on traditional decision analysis by modeling continuous variables with continuous, rather than discrete, distributions. A Bayesian analysis using a mixed discrete-continuous probability distribution for cigarette smoking rate is presented.
本文介绍了一种改进技术，用于对具有连续随机变量和概率相关性的风险与决策问题进行建模。变量采用称为“连续树”的四参数随机变量混合模型进行建模。随机变量的函数使用高斯求积法以一种称为“内华达模拟”（方差数值积分与概率相关性分析器）的方式进行计算。在分析技术、求解时间复杂度和准确性方面，将该技术与传统决策树建模进行了比较。内华达模拟利用决策问题中的概率独立性，同时允许概率相关性，从而为许多决策问题实现多项式计算时间复杂度。它通过采用比传统决策分析更大的近似值来提高传统决策树的准确性。它通过使用连续分布而非离散分布对连续变量进行建模，改进了传统决策分析。本文还给出了一个使用吸烟率的混合离散 - 连续概率分布的贝叶斯分析。
