论文标题
通过评估计算机模拟来促进专家知识的概率启发
Probabilistic elicitation of expert knowledge through assessment of computer simulations
论文作者
论文摘要
我们提出了一种新的方法，该方法使用人类专家的二元响应来评估来自统计模型的模拟数据的二进制响应，其中该参数会受到不确定性的影响。二进制响应描述了单个模拟的绝对现实主义或在OUT方法的两个替代版本中的一对模拟的相对现实主义。每个版本都在模型参数的值上提供了专家信念分布的非参数表示，而无需要求对参数值本身提出任何意见。我们的框架还集成了使用主动学习来有效查询专家的使用，并有可能提供有用的错误指定诊断。我们验证了两种方法，以判断二项式分布的自动专家，并验证人类专家，以判断美国和挪威各政党的选民分配。两种方法都提供了人类专家信念的灵活和有意义的表示，可以正确地确定挪威各方之间的选民更高分散。
We present a new method for probabilistic elicitation of expert knowledge using binary responses of human experts assessing simulated data from a statistical model, where the parameters are subject to uncertainty. The binary responses describe either the absolute realism of individual simulations or the relative realism of a pair of simulations in the two alternative versions of out approach. Each version provides a nonparametric representation of the expert belief distribution over the values of a model parameter, without demanding the assertion of any opinion on the parameter values themselves. Our framework also integrates the use of active learning to efficiently query the experts, with the possibility to additionally provide a useful misspecification diagnostic. We validate both methods on an automatic expert judging a binomial distribution, and on human experts judging the distribution of voters across political parties in the United States and Norway. Both methods provide flexible and meaningful representations of the human experts' beliefs, correctly identifying the higher dispersion of voters between parties in Norway.
