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Phase Transitions in Inference- [electronic resource]
![Phase Transitions in Inference - [electronic resource]](/Users/Baul/Images/book.png)
Phase Transitions in Inference- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101644
- ISBN
- 9798380366564
- DDC
- 004
- 서명/저자
- Phase Transitions in Inference - [electronic resource]
- 발행사항
- [S.l.]: : University of California, Berkeley., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(306 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
- 주기사항
- Advisor: Raghavendra, Prasad.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약What makes an algorithmic problem easy or hard? Many general algorithmic techniques arising from decades of research, along with the theory of NP-completeness based on reductions between hard problems, offers a good answer for problems where the input is ``worst-case''.However, this theory has very little to say when the input is random, and comprises of independent samples, as is frequently the case for problems in statistics. Statistical problems seemingly go through abrupt phase transitions in complexity, from hard to easy once the number of samples crosses a threshold. Understanding this boundary between ``hard'' and ``easy'' for statistical problems is still in nascent stages.This thesis comprises recent progress in understanding these phase transitions from the lens of semidefinite programming.
- 일반주제명
- Computer science.
- 일반주제명
- Computer engineering.
- 기타저자
- University of California, Berkeley Computer Science
- 기본자료저록
- Dissertations Abstracts International. 85-03B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798380366564
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aMohanty, Sidhanth.
■24510▼aPhase Transitions in Inference▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of California, Berkeley. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(306 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-03, Section: B.
■500 ▼aAdvisor: Raghavendra, Prasad.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aWhat makes an algorithmic problem easy or hard? Many general algorithmic techniques arising from decades of research, along with the theory of NP-completeness based on reductions between hard problems, offers a good answer for problems where the input is ``worst-case''.However, this theory has very little to say when the input is random, and comprises of independent samples, as is frequently the case for problems in statistics. Statistical problems seemingly go through abrupt phase transitions in complexity, from hard to easy once the number of samples crosses a threshold. Understanding this boundary between ``hard'' and ``easy'' for statistical problems is still in nascent stages.This thesis comprises recent progress in understanding these phase transitions from the lens of semidefinite programming.
■590 ▼aSchool code: 0028.
■650 4▼aComputer science.
■650 4▼aComputer engineering.
■653 ▼aAlgorithmic techniques
■653 ▼aAlgorithmic problem
■653 ▼aSemidefinite programming
■690 ▼a0984
■690 ▼a0464
■71020▼aUniversity of California, Berkeley▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g85-03B.
■773 ▼tDissertation Abstract International
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16934708▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
