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Efficient and Scalable Markov Chain Monte Carlo Methods and Its Biological Applications

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자료유형	E-Book
개인저자	Zhang, Yizhe.
단체저자명	Duke University. Computational Biology and Bioinformatics.
서명/저자사항	Efficient and Scalable Markov Chain Monte Carlo Methods and Its Biological Applications.
발행사항	[S.l.] : Duke University., 2018
발행사항	Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항	169 p.
소장본 주기	School code: 0066.
ISBN	9780355872736
일반주기	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B. Adviser: Lawerence Carin.
이용제한사항	This item is not available from ProQuest Dissertations & Theses.
요약	Markov Chain Monte Carlo (MCMC) stands as a fundamental approach for probabilistic inference in many computational statistics problems. Its application to computational biology and bioinformatics has attracted much attention in recent decades. A
요약	This thesis first focus on the theoretical connection (chapter 3), the unification and generalization of slice sampling and HMC. Base on these theoretical analysis, I present a generalized HMC that demonstrate efficient exploration of target dis
요약	The second part of the thesis, presented in chapter 4, concerns some advances remedying the practical issues of the generalized sampler, and how to scale up with large datasets. Chapter 4 first develops a novel scalable approximate sampling appr
요약	The remaining part of this thesis, consisting chapter 5 and chapter 6, discuss advances of scalable Bayesian method for some generic and core Biomedical applications. Two Bayesian inferential tasks involving latent variable model are discussed.
요약	Finally, chapter 7 concludes the dissertation and discussion some potential future studies in both methodology and applications.
일반주제명	Statistics. Bioinformatics. Computer science.
언어	영어
기본자료 저록	Dissertation Abstracts International79-09B(E). Dissertation Abstract International
대출바로가기	http://www.riss.kr/pdu/ddodLink.do?id=T14996804