A Summer School: Uncertainty and Variability in Ecological Inference, Forecasting, and Decision -- An Introduction to Modern Statistical Computation

暑期学校：生态推断、预测和决策中的不确定性和可变性——现代统计计算导论杜克大学克拉克，詹姆斯·s·杜克大学提议举办为期两周的研究生/研究生水平暑期学校，向生态学家和地球科学家介绍过去十年出现的现代统计计算技术。生态推断和预测受到在一系列尺度上运作的大量和多样的变异性来源的限制。分层贝叶斯和马尔可夫链蒙特卡罗模拟为分析具有多种不确定性和可变性来源的过程提供了强大的工具。然而，由于培训选择有限，这些技术在生态学方面的采用受到阻碍。在提议的暑期学校里，主要的统计学家和生态学家将提供一整天的演讲和计算技术的实践培训。学生将包括通过公开申请程序选择的高级研究生和博士后助理。他们将参加小型工作组，每个工作组将编写一章，连同课堂讲稿一起纳入出版的一册。通过培训一批经过挑选的年轻的定量生态学家和地球科学家，他们将反过来培训其他人，并在他们的研究中利用这些培训，这个暑期学校将加速现代统计计算技术的传播。此外，本课程将出版的卷将为更广泛的读者提供有用的参考资料。生态学家和地球科学家采用这些现代方法将加强生态预测领域，提高其声誉，并增加其对资源管理者和决策者的有用性。

A Summer School: Uncertainty and Variability in Ecological Inference, Forecasting, and Decision -- An Introduction to Modern Statistical ComputationClark, James S.Duke UniversityA two-week, graduate/post-graduate level summer school is proposed to introduce ecologists and earth scientists to modern statistical computation techniques that have emerged over the last decade. Ecological inference and forecasting are limited by large and diverse sources of variability that operate at a range of scales. Hierarchical Bayes and Markov chain Monte Carlo simulation provide powerful tools for analyzing processes characterized by multiple sources of uncertainty and variability. However, adoption of these techniques in ecology has been hindered due to limited training options. In the proposed summer school, leading statisticians and ecologists will provide day-long presentations and hands-on training with computation techniques. Students will include advanced graduate students and postdoctoral associates selected by an open application process. They will participate in small working groups that will each produce a chapter to be included in a published volume, together with lecture notes. By training a select group of young, quantitative ecologists and earth scientists, who will in turn train others as well as utilize this training in their research, this summer school will expedite the dissemination of modern statistical computation techniques. In addition, the volume that will be published as a result of this course will serve as a useful reference for a much broader audience. Adoption of these modern methods by ecologists and earth scientists will strengthen the field of ecological forecasting, enhance its reputation, and increase it usefulness to resource managers and policy makers.