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Automatic Discovery of Latent Clusters in General Regression Models

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자료유형E-Book
개인저자S. K., Minhazul Islam.
단체저자명University of Florida.
서명/저자사항Automatic Discovery of Latent Clusters in General Regression Models.
발행사항[S.l.] : University of Florida., 2017
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2017
형태사항108 p.
소장본 주기School code: 0070.
ISBN9780438122178
일반주기 Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Adviser: Arunava Banerjee.
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,
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
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
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
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
일반주제명Computer science.
언어영어
기본자료 저록Dissertation Abstracts International79-11B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T15000415

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