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Reducing the Price of Uncertainty: Scalable Computational Approaches for High-Dimensional Probabilistic Modeling
Reducing the Price of Uncertainty: Scalable Computational Approaches for High-Dimensional Probabilistic Modeling
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151443
- ISBN
- 9798382776187
- DDC
- 004
- 저자명
- Lin, Alexander.
- 서명/저자
- Reducing the Price of Uncertainty: Scalable Computational Approaches for High-Dimensional Probabilistic Modeling
- 발행사항
- [Sl] : Harvard University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 191 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: A.
- 주기사항
- Advisor: Ba, Demba.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2024.
- 초록/해제
- 요약Probabilistic models provide a principled way to model observed data, perform statistical inferences, and express uncertainty over latent variables. However, it remains computationally challenging to fit high-dimensional probabilistic models to data. In this dissertation, we provide a suite of approaches for reducing this computational burden for various probabilistic models of interest. We examine models along the entire spectrum of structured to flexible, tackling diverse examples such as sparse Bayesian learning, latent Gaussian models, log-concave densities, and mixture models. Our methodologies are a rich combination of mathematical developments that leverage tools spanning optimization and statistics, as well as computational advances such as parallel computing and automatic differentiation.
- 일반주제명
- Computer science
- 일반주제명
- Statistics
- 일반주제명
- Engineering
- 일반주제명
- Information science
- 키워드
- Gaussian models
- 기타저자
- Harvard University Engineering and Applied Sciences - Computer Science
- 기본자료저록
- Dissertations Abstracts International. 85-12A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382776187
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■0820 ▼a004
■1001 ▼aLin, Alexander.▼0(orcid)0000-0002-3490-0301
■24510▼aReducing the Price of Uncertainty: Scalable Computational Approaches for High-Dimensional Probabilistic Modeling
■260 ▼a[Sl]▼bHarvard University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a191 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: A.
■500 ▼aAdvisor: Ba, Demba.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2024.
■520 ▼aProbabilistic models provide a principled way to model observed data, perform statistical inferences, and express uncertainty over latent variables. However, it remains computationally challenging to fit high-dimensional probabilistic models to data. In this dissertation, we provide a suite of approaches for reducing this computational burden for various probabilistic models of interest. We examine models along the entire spectrum of structured to flexible, tackling diverse examples such as sparse Bayesian learning, latent Gaussian models, log-concave densities, and mixture models. Our methodologies are a rich combination of mathematical developments that leverage tools spanning optimization and statistics, as well as computational advances such as parallel computing and automatic differentiation.
■590 ▼aSchool code: 0084.
■650 4▼aComputer science
■650 4▼aStatistics
■650 4▼aEngineering
■650 4▼aInformation science
■653 ▼aProbabilistic models
■653 ▼aComputational approaches
■653 ▼aHigh-dimensional probabilistic models
■653 ▼aGaussian models
■690 ▼a0984
■690 ▼a0463
■690 ▼a0537
■690 ▼a0723
■71020▼aHarvard University▼bEngineering and Applied Sciences - Computer Science.
■7730 ▼tDissertations Abstracts International▼g85-12A.
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161777▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
