Mathematical Sciences: Bayesian Analysis, Chaos, Stochastic Optimization, and File-Merging Methodology

本课题研究的问题主要有四个方面 领域：随机优化，贝叶斯信息论 混沌模型中的分析、统计分析和问题 合并微数据文件。 随机优化方案 预计将开发一种基本自动化的， 一种求全局最小值的收敛算法 复杂的功能。 信息论研究 在贝叶斯分析领域致力于应用 各种信息措施对新理论的发展 在两个实验中选择更好的一个 调查 在混沌和统计属于贝叶斯统计 混沌非线性时间序列分析的发展 文件合并方法的统计理论， 在评估联邦机构的政策影响方面， 这是NSF过去支持的一个项目的延续。

The research problems in this project belong mainly to four areas: Stochastic Optimization, Information Theory in Bayesian Analysis, Statistical Analysis in Chaotic Models, and Problems in Merging Micro-data Files. The stochastic optimization project is expected to develop a mostly automated and possibly fast converging algorithm for finding the global minimum of a complicated function. The investigations in information theory in the Bayesian analysis area are devoted to the application of various information measures to the development of new theories for choosing the better of the two experiments. Investigations under Chaos and Statistics pertain to Bayesian statistical analysis of chaotic nonlinear times series. The development of statistical theory for file-merging methodology, which is used heavily in evaluating policy implications by federal agencies, is a continuation of a project supported by NSF in the past.