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Automatic Discovery of Latent Clusters in General Regression Models
内容资讯
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  
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Control Number  
joongbu:553944
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