CBMS Conference: Bayesian Modeling for Spatial and Spatio-Temporal Data

CBMS 会议：空间和时空数据的贝叶斯建模

• 批准号: 1642617
• 负责人: Athanasios Kottas
• 金额: $ 3.5万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1642617&HistoricalAwards=false
• 关键词: CBMS; Conference; Bayesian; Modeling; Spatial

This award supports the CMBS regional conference "Bayesian Modeling for Spatial and Spatio-Temporal Data" to be held at the University of California, Santa Cruz, August 14-18, 2017. The principal speaker is Alan Gelfand from Duke University. Hierarchical Bayesian methods for modeling spatial and spatio-temporal data constitute an extremely important area of research in the statistical sciences, with a wide range of applications. However, the explosive growth in all areas of spatial and spatio-temporal modeling has produced a massive body of literature that can be daunting for newcomers, imposing a steep entry barrier into the field. The conference lectures will facilitate introduction to the subject by providing a comprehensive review of Bayesian methods for spatial statistics. Established researchers, newcomers, and students will have the opportunity to learn and discuss the major ideas, modern modeling and computing methods, and recent results in the field. The conference will strengthen links and collaborations between multiple groups of researchers in the western United States. This award will support about 30 participants, with priority given to junior researchers.Additional lectures will be presented by Sudipto Banerjee (University of California, Los Angeles), Michele Guindani (University of California, Irvine), and Christopher Paciorek (University of California, Berkeley). All three speakers have research expertise in theory, methods, and applications of Bayesian modeling for spatial data, and their lectures will be constructed to be complementary to the main lectures.More information can be found at the conference webpage: https://cbms.soe.ucsc.edu

该奖项支持将于2017年8月14-18日在加州大学圣克鲁斯分校举行的CMBS区域会议“空间和时空数据的贝叶斯建模”。主讲人是杜克大学的Alan Gelfand。空间和时空数据建模的层次贝叶斯方法是统计科学中一个极其重要的研究领域，具有广泛的应用。然而，空间和时空建模所有领域的爆炸性增长已经产生了大量的文献，这些文献可能会让新手望而生畏，对进入该领域施加了陡峭的进入障碍。会议讲座将通过提供贝叶斯空间统计方法的全面回顾，促进对这一主题的介绍。既有研究人员、新手和学生将有机会学习和讨论该领域的主要思想、现代建模和计算方法以及最新成果。这次会议将加强美国西部多个研究小组之间的联系和合作。该奖项将支持约30名参与者，优先考虑初级研究人员。其他讲座将由Sudipto Banerjee（加州大学洛杉矶分校）、Michele Guindani（加州大学欧文分校）和Christopher Paciorek（加州大学伯克利分校）主讲。三位主讲人在空间数据贝叶斯建模的理论、方法和应用方面都有研究专长，他们的讲座将作为主要讲座的补充。更多信息请访问会议网页：https://cbms.soe.ucsc.edu