不确定性下的推理：一些Monte Carlo结果
Reasoning under Uncertainty: Some Monte Carlo Results
摘要：
一系列的蒙特卡洛研究进行比较的行为下的不确定性推理的一些替代程序。几个贝叶斯，线性模型和默认推理程序的行为进行了检查的背景下，不断增加的校准误差水平。最有趣的结果是，贝叶斯程序倾向于输出比其他技术更极端的后验信念值（后验信念接近0.0或1.0），但线性模型相对不太可能输出对错误结论的强有力支持。此外，会计证据项目之间的概率依赖性是重要的贝叶斯和线性更新程序。
英文摘要：
A series of monte carlo studies were performed to compare the behavior of some alternative procedures for reasoning under uncertainty. The behavior of several Bayesian, linear model and default reasoning procedures were examined in the context of increasing levels of calibration error. The most interesting result is that Bayesian procedures tended to output more extreme posterior belief values (posterior beliefs near 0.0 or 1.0) than other techniques, but the linear models were relatively less likely to output strong support for an erroneous conclusion. Also, accounting for the probabilistic dependencies between evidence items was important for both Bayesian and linear updating procedures.
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