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Volumn 13, Issue , 2012, Pages 1699-1703

Glm-ie: Generalised linear models inference & estimation toolbox

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE INFERENCE; BAYESIAN INFERENCE; EXPECTATION PROPAGATION; FULLY COMPATIBLE; GAUSSIANS; GRAPHICAL MODEL; LAZY EVALUATION; LEAST SQUARE; LEAST SQUARES ESTIMATION; MARKOV RANDOM FIELDS; MATRIX VECTOR MULTIPLICATION; MEAN FIELD; NON-GAUSSIAN; PENALTY FUNCTION; POTENTIAL FUNCTION; PROBABILISTIC CLASSIFICATION; PROBABILISTIC REGRESSION; VARIATIONAL BOUNDS;

EID: 84862002445     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (6)

References (15)
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  • 3
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    • Sparse MRI: The application of compressed sensing for rapid MR imaging
    • DOI 10.1002/mrm.21391
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  • 4
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    • Expectation propagation for approximate Bayesian inference
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    • (2001) UAI
    • Minka, T.1
  • 6
    • 71149097861 scopus 로고    scopus 로고
    • Convex variational Bayesian inference for large scale generalized linear models
    • Hannes Nickisch and Matthias Seeger. Convex variational Bayesian inference for large scale generalized linear models. In ICML, 2009.
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    • Nickisch, H.1    Seeger, M.2
  • 7
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    • Opper, M.1    Winther, O.2
  • 9
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    • September
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    • Rasmussen, C.E.1
  • 10
    • 84856673666 scopus 로고    scopus 로고
    • Large scale variational inference and experimental design for sparse generalized linear models
    • Matthias W. Seeger and Hannes Nickisch. Large scale variational inference and experimental design for sparse generalized linear models. SIAM Journal on Imaging Sciences, 4(1):166-199, 2011.
    • (2011) SIAM Journal on Imaging Sciences , vol.4 , Issue.1 , pp. 166-199
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  • 11
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    • Bayesian experimental design of magnetic resonance imaging sequences
    • Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann, and Bernhard Schölkopf. Bayesian experimental design of magnetic resonance imaging sequences. In NIPS, 2009.
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  • 12
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    • Optimization of kspace trajectories for compressed sensing by bayesian experimental design
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.