상세정보
Export to Refworks부가기능
Automatic Discovery of Latent Clusters in General Regression Models
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Automatic Discovery of Latent Clusters in General Regression Models. |
| 개인저자 | S. K., Minhazul Islam. |
| 단체저자명 | University of Florida. |
| 발행사항 | [S.l.]: University of Florida., 2017. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2017. |
| 형태사항 | 108 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-11B(E). Dissertation Abstract International |
| ISBN | 9780438122178 |
| 학위논문주기 | Thesis (Ph.D.)--University of Florida, 2017. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Adviser: Arunava Banerjee. |
| 요약 | We present a flexible nonparametric Bayesian framework for automatic detection of local clusters in general regression models. The models are built using techniques that are now considered standard in statistical parameter estimation literature, |
| 요약 | In the first part of this thesis, we formulate all traditional versions of the infinite mixture of GLM models under the Dirichlet Process framework. We study extensively two different inference techniques for these models, namely, variational in |
| 요약 | In the second part, we present a flexible nonparametric generative model for multigroup regression that detects latent common clusters of groups. We name this "Infinite MultiGroup Generalized Linear Model" (iMG-GLM). We present two versions of t |
| 요약 | In the third part, we present a flexible nonparametric generative model for multilevel regression that strikes an automatic balance between identifying common effects across groups while respecting their idiosyncrasies. We name it "Infinite Mixt |
| 요약 | For the final problem we present a framework that shows how infinite mixtures of Linear Regression (Dirichlet Process mixtures) can be used to design a new denoising technique in the domain of time series data that presumes a model for the uncor |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
