서브메뉴
검색
Automatic Discovery of Latent Clusters in General Regression Models
Automatic Discovery of Latent Clusters in General Regression Models
- 자료유형
- 학위논문
- Control Number
- 0015000415
- International Standard Book Number
- 9780438122178
- Dewey Decimal Classification Number
- 004
- Main Entry-Personal Name
- S. K., Minhazul Islam.
- Publication, Distribution, etc. (Imprint
- [Sl] : University of Florida, 2017
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2017
- Physical Description
- 108 p
- General Note
- Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
- General Note
- Adviser: Arunava Banerjee.
- Dissertation Note
- Thesis (Ph.D.)--University of Florida, 2017.
- Summary, Etc.
- 요약We 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,
- Summary, Etc.
- 요약In 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
- Summary, Etc.
- 요약In 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
- Summary, Etc.
- 요약In 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
- Summary, Etc.
- 요약For 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
- Subject Added Entry-Topical Term
- Computer science
- Added Entry-Corporate Name
- University of Florida.
- Host Item Entry
- Dissertation Abstracts International. 79-11B(E).
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:553944