| LDR | | 02673nam u200481 4500 |
| 001 | | 000000418344 |
| 005 | | 20190215162803 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780355872736 |
| 035 | |
▼a (MiAaPQ)AAI10742961 |
| 035 | |
▼a (MiAaPQ)duke:14392 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Zhang, Yizhe. |
| 245 | 10 |
▼a Efficient and Scalable Markov Chain Monte Carlo Methods and Its Biological Applications. |
| 260 | |
▼a [S.l.]:
▼b Duke University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 169 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B. |
| 500 | |
▼a Adviser: Lawerence Carin. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Duke University, 2018. |
| 506 | |
▼a This item is not available from ProQuest Dissertations & Theses. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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. |
| 520 | |
▼a Finally, chapter 7 concludes the dissertation and discussion some potential future studies in both methodology and applications. |
| 590 | |
▼a School code: 0066. |
| 650 | 4 |
▼a Statistics. |
| 650 | 4 |
▼a Bioinformatics. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0463 |
| 690 | |
▼a 0715 |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a Duke University.
▼b Computational Biology and Bioinformatics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-09B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0066 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996804
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |