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Spatio-Temporal Event Modeling Through Deep Kernel-Based Point Processes.
Spatio-Temporal Event Modeling Through Deep Kernel-Based Point Processes.
상세정보
- 자료유형
- 학위논문(국외)
- 기본표목-개인명
- 표제와 책임표시사항
- Spatio-Temporal Event Modeling Through Deep Kernel-Based Point Processes.
- 발행, 배포, 간사 사항
- 발행, 배포, 간사 사항
- 형태사항
- 194 p.
- 일반주기
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 일반주기
- Advisor: Xie, Yao.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2024.
- 요약 등 주기
- 요약As the data volume and complexity in modern applications continue to grow, there is an increasing need in parallel for advanced point process models that can effectively capture intricate event dependencies and dynamics. This thesis focuses on advancing point process modeling by developing deep influence kernels for spatio-temporal event data. Combining statistical modeling principles with the expressive power of deep learning, the proposed methods effectively capture complex event dependencies, improve model estimation efficiency, and enhance interpretability. The thesis also demonstrates the practicality of deep kernel-based point processes in various real-world applications, such as in modeling COVID-19 transmission dynamics and urban crime events1.I hope that the contributions presented here will not only extend to a broader range of methodological and real-world applications but also inspire future research in the rapidly evolving area of spatio-temporal event modeling and neural point processes. For instance, when looking from a methodological standpoint, using neural networks as a flexible tool offers an opportunity to investigate more complex, higher-order statistics of point process models. On the application side, the use of neural networks allows the integration of comprehensive external data sources within the statistical frameworks, such as demographic or mobility data, to enhance the realism of real-world implementations. The neural point processes also have the potential to be adopted in controlled experiments for the study of variable effects, providing researchers with diverse options. These models could assist domain experts by suggesting new hypotheses derived from robust statistical perspectives, motivating interdisciplinary collaboration.
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 부출표목-단체명
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 원문정보보기
MARC
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■006m o d
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■020 ▼a9798263397128
■035 ▼a(MiAaPQ)AAI32316029
■035 ▼a(MiAaPQ)GeorgiaTech76953
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a364
■1001 ▼aDong, Zheng.
■24510▼aSpatio-Temporal Event Modeling Through Deep Kernel-Based Point Processes.
■260 ▼a[S.l.]▼bGeorgia Institute of Technology. ▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a194 p.
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Xie, Yao.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2024.
■520 ▼aAs the data volume and complexity in modern applications continue to grow, there is an increasing need in parallel for advanced point process models that can effectively capture intricate event dependencies and dynamics. This thesis focuses on advancing point process modeling by developing deep influence kernels for spatio-temporal event data. Combining statistical modeling principles with the expressive power of deep learning, the proposed methods effectively capture complex event dependencies, improve model estimation efficiency, and enhance interpretability. The thesis also demonstrates the practicality of deep kernel-based point processes in various real-world applications, such as in modeling COVID-19 transmission dynamics and urban crime events1.I hope that the contributions presented here will not only extend to a broader range of methodological and real-world applications but also inspire future research in the rapidly evolving area of spatio-temporal event modeling and neural point processes. For instance, when looking from a methodological standpoint, using neural networks as a flexible tool offers an opportunity to investigate more complex, higher-order statistics of point process models. On the application side, the use of neural networks allows the integration of comprehensive external data sources within the statistical frameworks, such as demographic or mobility data, to enhance the realism of real-world implementations. The neural point processes also have the potential to be adopted in controlled experiments for the study of variable effects, providing researchers with diverse options. These models could assist domain experts by suggesting new hypotheses derived from robust statistical perspectives, motivating interdisciplinary collaboration.
■590 ▼aSchool code: 0078.
■650 4▼aCrime.
■650 4▼aSepsis.
■650 4▼aNeural networks.
■650 4▼aCOVID-19.
■690 ▼a0800
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360668▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
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