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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 서양
- 최종처리일시
- 20260202105603
- ISBN
- 9798263397128
- DDC
- 364
- 저자명
- Dong, Zheng.
- 서명/저자
- Spatio-Temporal Event Modeling Through Deep Kernel-Based Point Processes
- 발행사항
- [Sl] : Georgia Institute of Technology, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 194 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Xie, Yao.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2024.
- 초록/해제
- 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.
- 일반주제명
- Crime
- 일반주제명
- Sepsis
- 일반주제명
- Neural networks
- 일반주제명
- COVID-19
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260202105603
■006m o d
■007cr#unu||||||||
■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[Sl]▼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.
