Workshop: Intensive, interdisciplinary short courses on Bayesian inference for ecologists

研讨会：针对生态学家的贝叶斯推理强化、跨学科短期课程

• 批准号: 2042028
• 负责人: N. Thompson Hobbs
• 金额: $ 41.23万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2042028&HistoricalAwards=false
• 关键词: Workshop; Intensive; interdisciplinary; short; courses

Progress in environmental biology requires application of statistical methods to gain understanding from models and data. Models describe scientific hypotheses about environmental processes in mathematical terms. Statistics allow researchers to assess the value of their data as evidence supporting or refuting the hypotheses expressed in those models. This procedure is fundamental to all scientific inquiry, but many modern researchers at all stages of their careers lack the training needed to apply it confidently. This award will provide intensive training in Bayesian statistical modeling to graduate students, post-docs, university faculty, and agency researchers. Participants will be actively recruited from groups underrepresented in science. This training will enhance participants’ ability to advance science and to solve complex, environmental problems confronting society and will foster collaborations between ecologists and statisticians.The training workshops will teach basic statistical theory needed to understand Bayesian inference, train participants to write proper mathematical expressions for Bayesian models, enable participants to use Bayesian methods to solve a wide range of analysis problems and disseminate materials to enhance teaching by others. The training will emphasize understanding basic principles of Bayesian modeling using lectures and laboratory exercises during three, annually offered short courses spanning two weeks each. Topics to be covered include probability and distribution theory, Bayes theorem and its relationship to maximum likelihood, writing simple and hierarchical Bayesian models, the Markov chain Monte Carlo algorithm, and model checking. Specialized, advanced topics will be chosen to match the specific interests of course participants. Future collaborations between participants and statisticians will be encouraged by including six statistics graduate students to assist with problem solving during courses. The training will enhance the intellectual capital of the disciplines of environmental biology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

环境生物学的进步要求应用统计学方法从模型和数据中获得理解。模型用数学术语描述了关于环境过程的科学假设。统计学允许研究人员评估他们的数据的价值，作为支持或反驳这些模型中表达的假设的证据。这一程序是所有科学研究的基础，但许多现代研究人员在其职业生涯的各个阶段都缺乏自信地应用这一程序所需的培训。该奖项将为研究生、博士后、大学教师和机构研究人员提供贝叶斯统计建模方面的强化培训。参与者将从科学界代表性不足的群体中积极招募。培训工作坊将教授理解贝叶斯推理所需的基本统计理论，训练参加者为贝叶斯模型编写正确的数学表达式，使参加者能够使用贝叶斯方法来解决广泛的分析问题，并分发材料以加强其他人的教学。培训将强调通过讲座和实验室练习了解贝叶斯建模的基本原理，为期三天，每年提供为期两周的短期课程。将涵盖的主题包括概率和分布理论、贝叶斯定理及其与最大似然的关系、编写简单和分层的贝叶斯模型、马尔可夫链蒙特卡罗算法和模型检验。将根据学员的具体兴趣选择专门的、高级的主题。学员和统计学家之间未来的合作将受到鼓励，将包括六名统计学研究生，以帮助在课程期间解决问题。培训将提高环境生物学学科的智力资本。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。