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Volumn 33, Issue , 2014, Pages 356-364

Tilted variational bayes

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC);

EID: 84955490168     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (9)

References (17)
  • 6
    • 25444528713 scopus 로고    scopus 로고
    • Assessing approximate inference for binary Gaussian process classification
    • Malte Kuss and Carl Edward Rasmussen. Assessing approximate inference for binary Gaussian process classification. The Journal of Machine Learning Research, 6:1679-1704, 2005.
    • (2005) The Journal of Machine Learning Research , vol.6 , pp. 1679-1704
    • Kuss, M.1    Rasmussen, C.E.2
  • 7
    • 0345978970 scopus 로고    scopus 로고
    • Expectation propagation for approximate Bayesian inference
    • Jack S. Breese and Daphne Koller, editors San Francisco, CA Morgan Kauffman
    • Thomas P. Minka. Expectation propagation for approximate Bayesian inference. In Jack S. Breese and Daphne Koller, editors, Uncertainty in Artificial Intelligence, Volume 17, San Francisco, CA, 2001. Morgan Kauffman.
    • (2001) Uncertainty in Artificial Intelligence , vol.17
    • Minka, T.P.1
  • 13
    • 27844592624 scopus 로고    scopus 로고
    • Variational inference for student-t models: Robust Bayesian interpolation and generalised component analysis
    • Michael E Tipping and Neil D Lawrence. Variational inference for student-t models: Robust bayesian interpolation and generalised component analysis. Neurocomputing, 69(1):123-141, 2005.
    • (2005) Neurocomputing , vol.69 , Issue.1 , pp. 123-141
    • Tipping, M.E.1    Lawrence, N.D.2
  • 14
    • 65749118363 scopus 로고    scopus 로고
    • Graphical models, exponential families, and variational inference
    • Martin J Wainwright and Michael I Jordan. Graphical models, exponential families, and variational inference. Foundations and Trends® in Machine Learning, 1(1-2):1-305, 2008.
    • (2008) Foundations and Trends® in Machine Learning , vol.1 , Issue.1-2 , pp. 1-305
    • Wainwright, M.J.1    Jordan, M.I.2
  • 15
    • 85156191859 scopus 로고    scopus 로고
    • Bayesian methods for mixtures of experts
    • David Touretzky, Michael Mozer, and Mark Hasselmo, editors Cambridge, MA MIT Press
    • Steve Waterhouse, David J. C. MacKay, and Tony Robinson. Bayesian methods for mixtures of experts. In David Touretzky, Michael Mozer, and Mark Hasselmo, editors, Advances in Neural Information Processing Systems, Volume 8, pages 351-357, Cambridge, MA, 1996. MIT Press.
    • (1996) Advances in Neural Information Processing Systems , vol.8 , pp. 351-357
    • Waterhouse, S.1    MacKay, D.J.C.2    Robinson, T.3
  • 17
    • 0031345518 scopus 로고    scopus 로고
    • Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization
    • Ciyou Zhu, Richard H Byrd, Peihuang Lu, and Jorge Nocedal. Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on Mathematical Software (TOMS), 23(4):550-560, 1997.
    • (1997) ACM Transactions on Mathematical Software (TOMS) , vol.23 , Issue.4 , pp. 550-560
    • Zhu, C.1    Byrd, R.H.2    Lu, P.3    Nocedal, J.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.