Collaborative Research on Flexible Modeling and Analysis for Information Aggregation

在许多问题中，信息可以从许多不同的来源获得。汇总这些不同的信息对于良好的决策是重要的，但也会导致在开发描述信息的模型时出现特殊的统计问题。以前在这一领域的研究主要依靠使用历史数据作为模拟信息源的基础。这项拟议的研究开发了一个贝叶斯框架，决策者可以使用该框架来编码他关于信息源的主观知识，以便聚合它们的信息。这一框架具有高度灵活的环境，可对信息源的概率性质和相互关系进行建模，并要求使用经过验证的决策分析评估技术作出直截了当和直观的主观判断。通过分析、数值或蒙特卡罗技术完成对评估的分析，以产生感兴趣事件的后验分布。

In many problems, information is available from many different sources. Aggregating these diverse bits of information is important to good decision-making but also leads to special statistical problems in developing models to characterize the information. Prior research in this areas has relied primarily on the use of historical data as a basis for modelling the information sources. The proposed research develops a Bayesian framework that a decision maker can use to encode his subjective knowledge about the information sources in order to aggregate their information. This framework features a highly flexible environment for modelling the probabilistic nature and interrelationships of the information sources and requires straightforward and intuitive subjective judgements using proven decision-analytic assessment techniques. Analysis of the assessments to produce a posterior distribution for the event of interest is accomplished through analytical, numerical or Monte Carlo techniques.