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Volumn 2, Issue January, 2014, Pages 1547-1555

Decoupled variational Gaussian inference

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; INFERENCE ENGINES; LINEAR REGRESSION; REGRESSION ANALYSIS;

EID: 84937864160     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (8)

References (24)
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    • 77952563025 scopus 로고    scopus 로고
    • Variational inference for large-scale models of discrete choice
    • M. Braun and J. McAuliffe. Variational inference for large-scale models of discrete choice. Journal of the American Statistical Association, 105(489): 324-335, 2010.
    • (2010) Journal of the American Statistical Association , vol.105 , Issue.489 , pp. 324-335
    • Braun, M.1    McAuliffe, J.2
  • 4
    • 19844373567 scopus 로고    scopus 로고
    • A globally convergent linearly constrained lagrangian method for nonlinear optimization
    • Michael P Friedlander and Michael A Saunders. A globally convergent linearly constrained lagrangian method for nonlinear optimization. SIAM Journal on Optimization, 15(3): 863-897, 2005.
    • (2005) SIAM Journal on Optimization , vol.15 , Issue.3 , pp. 863-897
    • Friedlander, M.P.1    Saunders, M.A.2
  • 10
  • 17
    • 63249135864 scopus 로고    scopus 로고
    • The variational Gaussian approximation revisited
    • M. Opper and C. Archambeau. The variational gaussian approximation revisited. Neural Computation, 21(3): 786-792, 2009.
    • (2009) Neural Computation , vol.21 , Issue.3 , pp. 786-792
    • Opper, M.1    Archambeau, C.2
  • 19
    • 0000061021 scopus 로고
    • A quadratically-convergent algorithm for general nonlinear programming problems
    • Stephen M Robinson. A quadratically-convergent algorithm for general nonlinear programming problems. Mathematical programming, 3(1): 145-156, 1972.
    • (1972) Mathematical Programming , vol.3 , Issue.1 , pp. 145-156
    • Robinson, S.M.1
  • 21
    • 44649181578 scopus 로고    scopus 로고
    • Bayesian inference and optimal design in the sparse linear model
    • M. Seeger. Bayesian Inference and Optimal Design in the Sparse Linear Model. Journal of Machine Learning Research, 9: 759-813, 2008.
    • (2008) Journal of Machine Learning Research , vol.9 , pp. 759-813
    • Seeger, M.1
  • 22
    • 74349098311 scopus 로고    scopus 로고
    • Sparse linear models: Variational approximate inference and Bayesian experimental design
    • M. Seeger. Sparse linear models: Variational approximate inference and Bayesian experimental design. Journal of Physics: Conference Series, 197(012001), 2009.
    • (2009) Journal of Physics: Conference Series , vol.197
    • Seeger, M.1
  • 23
    • 84856673666 scopus 로고    scopus 로고
    • Large scale Bayesian inference and experimental design for sparse linear models
    • M. Seeger and H. Nickisch. Large scale Bayesian inference and experimental design for sparse linear models. SIAM Journal of Imaging Sciences, 4(1): 166-199, 2011.
    • (2011) SIAM Journal of Imaging Sciences , vol.4 , Issue.1 , pp. 166-199
    • Seeger, M.1    Nickisch, H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.