Inference in the presence of constraints

该项目的主要目标之一是在数据有限但有科学专家输入的情况下改善曲线和曲面的估计。为了灵活地对数据建模，存在着许多平滑技术，这些技术都具有良好的性能。有可能将这些技术应用于根据以前可用的科学进行调整的数据形式。根据科学的准确性，由此得出的估计值应该比从未经调整的数据中获得的估计值更好。考虑到定性信息而修改数据的技术称为数据锐化。通过使用数据锐化，科学家应该能够获得他们感兴趣的数量的改进估计（通常以曲线和曲面的形式），并且他们也应该能够获得更好的预测结果。为了处理各种各样的建模问题，将开发用于数据锐化的软件。

One of the main goals of this project is to improve curve and surface estimation when data are limited but when input from scientific experts is available. A number of smoothing techniques exist for flexibly modeling data, and these techniques have good properties. It is possible to apply these techniques to forms of data which have been adjusted according to previously available science. Depending on the accuracy of the science, the resulting estimates should be better than those obtained from the unadjusted data. The technique of modifying the data, taking into account qualitative information, is called data sharpening. By employing data sharpening, scientists should be able to obtain improved estimates of quantities they are interested in (usually in the form of curves and surfaces), and they should also be able to obtain better predictions as a result. Software for data sharpening will be developed in order to handle a wide variety of modeling problems.