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Finite Sample Bounds and Path Selection for Sequential Monte Carlo
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Finite Sample Bounds and Path Selection for Sequential Monte Carlo. |
| 개인저자 | Marion, Joseph. |
| 단체저자명 | Duke University. Statistical Science. |
| 발행사항 | [S.l.]: Duke University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 118 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-02B(E). Dissertation Abstract International |
| ISBN | 9780438377356 |
| 학위논문주기 | Thesis (Ph.D.)--Duke University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Adviser: Scott C. Schmidler. |
| 요약 | Sequential Monte Carlo (SMC) samplers have received attention as an alternative to Markov chain Monte Carlo for Bayesian inference problems due to their strong empirical performance on difficult multimodal problems, natural synergy with parallel |
| 요약 | In this thesis, we provide conditions under which SMC provides a randomized approximation scheme, showing how to choose the number of of particles and Markov kernel transitions at each SMC step in order to ensure an accurate approximation with b |
| 요약 | A key advantage of this approach is that the bounds provide insight into the selection of efficient sequences of SMC distributions. When the target distribution is spherical Gaussian or log-concave, we show that judicious selection of interpolat |
| 요약 | Selecting efficient sequences of distributions is a problem that also arises in the estimation of normalizing constants using path sampling. In the final chapter of this thesis, we develop automatic methods for choosing sequences of distribution |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
