CBMS Conference: Bayesian Forecasting and Dynamic Models

该奖项支持由加州大学圣克鲁斯分校统计系于2020年8月10日至14日举办的2020年NSF-CBMS会议“贝叶斯预测和动态模型”。这次会议将有三位主要讲师，他们将发表10场主要演讲。来自杜克大学的Mike West教授是贝叶斯预测和动态模型领域的基础研究人员和主要参考人员，他将发表7场主要讲座。来自Insper的赫迪伯特·洛佩斯教授和来自加州大学洛杉矶分校的拉克尔·普拉多教授将发表三场主要讲座。会议还将举办一个具体应用领域的案例研究会议，让初级与会者了解为高度结构化的时间序列数据开发有重点的统计工具的过程。此外，会议还将提供实用数据分析方面的“实践”课程，以及与来自北加州公司的行业专家进行的小组讨论。这将为参与者提供更多关于贝叶斯预测和动态模型如何在实际非学术环境中应用的机会。资深和初级研究人员、博士后研究员和学生将有机会学习和讨论贝叶斯时间序列和动态建模领域的主要基本思想以及最新和现代模型和计算方法。这次会议旨在吸引新的研究人员进入这一领域。此外，鉴于会议的区域重点，预计会议将为加强美国西部多个团体之间的联系和合作提供一个重要机会。时态数据的适当建模和预测，特别是在高维环境中，在广泛的应用中是关键。多年来，这一领域确定了数学和统计科学的一个主要研究领域，并导致在使用这些方法的方法学、计算和应用领域开展密集的研究活动。特别是，这一领域最近的重要研究进展产生了大量文献，其中包括用于分析和预测时间序列数据的新的复杂模型和方法，以及用于有效地推断和预测的强大的计算工具和相关软件。对于新手来说，探索、理解和应用这些模型和工具可能是一项艰巨的任务，为该领域设置了一个陡峭的障碍。本次会议及其衍生的专著将通过提供对贝叶斯建模和预测工具的全面审查来促进对该领域的介绍。有关更多信息，请参阅会议网页：http://cbms.soe.ucsc.edu/2020/This奖反映了美国国家科学基金会的法定使命，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。

This award supports the 2020 NSF-CBMS conference "Bayesian forecasting and dynamic models" hosted by the Department of Statistics at the University of California Santa Cruz, during August 10-14, 2020. The conference will feature three principal lecturers that will deliver 10 main lectures. Professor Mike West from Duke University, who is a foundational researcher and a major reference in the field of Bayesian forecasting and dynamic models will deliver 7 main lectures. Professor Hedibert Lopes from Insper and Professor Raquel Prado from UCSC will deliver 3 main lectures. The conference will also feature a case-study session in a specific area of application to expose junior participants to the process of developing focused statistical tools for highly structured time series data. In addition, the conference will offer "hands-on" sessions on practical data analysis and a panel session with industry experts from companies in Northern California. This will provide participants additional exposure on how Bayesian forecasting and dynamic models are applied in practical non-academic settings. Established and junior researchers, postdoctoral fellows and students will have the opportunity to learn and discuss the major foundational ideas as well as recent and modern models and computing methods in the area of Bayesian time series and dynamic modeling. The conference aims to attract new researchers to this field. Furthermore, given the regional emphasis of the conference, it is expected that the conference will provide an important opportunity for strengthening links and collaborations between multiple groups in the Western United States. Adequate modeling and forecasting of temporal data, particularly in large-dimensional settings, is key in a wide range of applications. This area has defined a major research arena in the mathematical and statistical sciences for years and has also led to intense research activity in methodological, computational and applied areas where these methods are used. In particular, recent important research advances in this area have led to a massive body of literature that comprise new sophisticated models and methods for analysis and forecasting of time series data, as well as powerful computational tools and related software for inference and forecasting in an efficient manner. Exploring, understanding, and applying these models and tools can be a daunting task for newcomers, imposing a steep barrier into the field. This conference, along with the monograph derived from it will facilitate introduction to the area by providing a comprehensive review of Bayesian modeling and forecasting tools.For more information, please refer to the conference webpage: http://cbms.soe.ucsc.edu/2020/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.