Conference on Long-Range Dependence, Self-Similarity, and Heavy Tails
长程依赖、自相似性和重尾会议
基本信息
- 批准号:1208965
- 负责人:
- 金额:$ 0.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2012
- 资助国家:美国
- 起止时间:2012-04-01 至 2013-03-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The PI is co-organizing a conference on long-range dependence, self-similarity, and heavy tails, which will take place in Research Triangle Park, North Carolina in April 2012. More details can be found on the conference web-site http://lrd2012.web.unc.edu. The three themes of the conference are studied and arise across a wide range of disciplines and applications, including probability, statistics, geophysics, telecommunications, engineering, economics, finance and insurance. Many of these fields were literally transformed by the use of models involving long-range dependence and heavy tails. For example, with a dramatic shift from circuit to packet switching in modern communication networks, these models took over the dominant place from the models based on Poisson assumptions and exponential distributions. New exciting areas of application such as social networks are posing new challenges where power laws, scaling limits, and self-similarity phenomena emerge in a new light. The conference will feature talks from world renowned experts as well as poster presentations by students and junior researchers. It is expected to have an important and long lasting scientific, educational, and socioeconomic impact. The conference will foster the exchange of ideas between probabilists, theoretical and applied statisticians, other scientists, as well as students and practitioners. This will likely lead to formulating new problems relevant to important applications, thereby stimulating the development of new fundamental theoretical research. Many of these problems are expected to have a broad educational, scientific, and general impact on society. For example, in connection to understanding risk, gauging uncertainty in complex setting, or taking advantage of emerging network structures. Most importantly, the event would introduce young researchers and graduate students to important areas of research.
PI 正在联合组织一次关于长程依赖、自相似性和重尾的会议,该会议将于 2012 年 4 月在北卡罗来纳州三角研究园举行。更多详细信息可以在会议网站 http: //lrd2012.web.unc.edu。会议的三个主题涉及广泛的学科和应用,包括概率、统计学、地球物理学、电信、工程、经济学、金融和保险。其中许多领域实际上是通过使用涉及长程依赖和重尾的模型而发生改变的。例如,随着现代通信网络从电路交换到分组交换的巨大转变,这些模型从基于泊松假设和指数分布的模型中占据了主导地位。社交网络等令人兴奋的新应用领域正在带来新的挑战,幂律、尺度限制和自相似现象以新的视角出现。会议将包括世界知名专家的演讲以及学生和初级研究人员的海报展示。 预计它将产生重要且持久的科学、教育和社会经济影响。该会议将促进概率学家、理论和应用统计学家、其他科学家以及学生和从业者之间的思想交流。这可能会导致提出与重要应用相关的新问题,从而刺激新的基础理论研究的发展。其中许多问题预计将对社会产生广泛的教育、科学和普遍影响。例如,与理解风险、衡量复杂环境中的不确定性或利用新兴网络结构有关。最重要的是,该活动将向年轻研究人员和研究生介绍重要的研究领域。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Stilian Stoev其他文献
Stilian Stoev的其他文献
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{{ truncateString('Stilian Stoev', 18)}}的其他基金
Collaborative Research: IMR: MM-1A: Scalable Statistical Methodology for Performance Monitoring, Anomaly Identification, and Mapping Network Accessibility from Active Measurements
合作研究:IMR:MM-1A:用于性能监控、异常识别和主动测量映射网络可访问性的可扩展统计方法
- 批准号:
2319592 - 财政年份:2023
- 资助金额:
$ 0.5万 - 项目类别:
Continuing Grant
ATD: Collaborative Research: Extremal Dependence and Change-Point Detection Methods for High-Dimensional Data Streams with Applications to Network Cybersecurity
ATD:协作研究:高维数据流的极端依赖性和变点检测方法及其在网络网络安全中的应用
- 批准号:
1830293 - 财政年份:2018
- 资助金额:
$ 0.5万 - 项目类别:
Continuing Grant
FRG: Collaborative Research: Extreme value theory for spatially indexed functional data
FRG:协作研究:空间索引函数数据的极值理论
- 批准号:
1462368 - 财政年份:2015
- 资助金额:
$ 0.5万 - 项目类别:
Continuing Grant
EVA 2015: The 9th International Conference on Extreme Value Analysis
EVA 2015:第九届国际极值分析会议
- 批准号:
1512982 - 财政年份:2015
- 资助金额:
$ 0.5万 - 项目类别:
Standard Grant
Spatio-Temporal Dependence and Extremes with Applications to Networking and the Environment
时空依赖性和极端情况及其在网络和环境中的应用
- 批准号:
1106695 - 财政年份:2011
- 资助金额:
$ 0.5万 - 项目类别:
Continuing Grant
Extremes: Short and Long-Range Dependence; Modeling and Inference with Applications to Computer Networks and Risk Analysis
极端情况:短期和长期依赖性;
- 批准号:
0806094 - 财政年份:2008
- 资助金额:
$ 0.5万 - 项目类别:
Continuing Grant
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