Conference: EnviBayes Workshop on Complex Environmental Data

会议：复杂环境数据 EnviBayes 研讨会

• 批准号: 2327107
• 负责人: Andrea Kaplan
• 金额: $ 2万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327107&HistoricalAwards=false
• 关键词: Conference; EnviBayes; Workshop; Complex; Environmental

This workshop titled "EnviBayes Workshop on Complex Environmental Data" will be held in Fort Collins, Colorado on September 18-20, 2023. Environmental and ecological data are often complex and diverse. This complexity may arise from intricate data-generating mechanisms, such as the presence of many interacting species in an ecological study, or from structural dependence, such as correlation in space and time of air pollution measurements. As such, analysis of environmental and ecological datasets may require specialized statistical and computational tools to appropriately capture and account for the complexity. While the ecological and environmental statistics community is not large, it is easy to become siloed in each of the respective sub-communities. However, the advances in modeling approaches and tools for one type of dependence (and data) can often be usefully extended to another type of dependence and have a large impact on multiple application areas. It can therefore benefit statisticians working with different facets of environmental and ecological data to interact with each other to propagate knowledge among our sub-communities for the increased advancement of all disciplines. The workshop will have the following major aims: i) to provide sub-communities within environmental and ecological statistics with a venue to share cutting-edge advancements within their own networks to further spark deeper knowledge gains; ii) to transfer knowledge between sub-communities and foster collaboration.The program of the EnviBayes Workshop on Complex Environmental Data includes: i) Environmental Health and Epidemiology; ii) Data from Remote Sensing; iii) Models for Spatial and Temporal Dependence; iv) Multivariate and Spatial Extremes; v) Using Climate Models to Project Future Extremes; vi) Models for Animal Movement and Abundance; vii) Statistical Inference Related to Complex Computer Models. This project will encourage the participation of graduate students, early-career researchers, and members of groups under-represented in statistics. More details about the workshop can be found at: https://statistics.colostate.edu/envibayes-workshop/.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.

这个研讨会题为“EnviBayes复杂环境数据研讨会”将于2023年9月18日至20日在科罗拉多的柯林斯堡举行。环境和生态数据往往复杂多样。这种复杂性可能来自复杂的数据生成机制，例如在生态研究中存在许多相互作用的物种，或者来自结构依赖性，例如空气污染测量的空间和时间相关性。 因此，环境和生态数据集的分析可能需要专门的统计和计算工具，以适当地捕捉和解释复杂性。虽然生态和环境统计界的规模不大，但很容易在各自的次级界形成孤立。然而，一种类型的依赖（和数据）的建模方法和工具的进步通常可以有效地扩展到另一种类型的依赖，并对多个应用领域产生巨大影响。因此，它可以使从事环境和生态数据不同方面工作的统计人员受益，使他们彼此互动，在我们的次级社区中传播知识，以促进所有学科的进步。该讲习班将有以下主要目标：i）为环境和生态统计中的次级社区提供一个场所，以便在其自己的网络内分享尖端进展，以进一步激发更深层次的知识增益; ii）在次级社区之间转移知识并促进合作。ii）遥感数据; iii）空间和时间依赖模型; iv）多变量和空间极端; v）使用气候模型预测未来极端; vi）动物运动和迁徙模型; vii）与复杂计算机模型相关的统计推断。该项目将鼓励研究生、早期职业研究人员和统计学中代表性不足的群体成员的参与。关于研讨会的更多细节可以在以下网站找到：https://statistics.colostate.edu/envibayes-workshop/.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。