Variational-Bayes .org

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2003

Beal, M.J. Variational Algorithms for Approximate Bayesian Inference pdf html PhD. Thesis

Beal, M.J. and Ghahramani, Z. The Variational Bayesian EM Algorithm for Incomplete Data: with Application to Scoring Graphical Model Structures pdf ps.gz Bayesian Statistics 7, Oxford University Press, 2003.

Watanabe, S., Minami, Y., Nakamura, A. and Ueda, N. Application of Variational Bayesian Approach to Speech Recognition ps.gz pdf NIPS 15

Winn, J. Variational Message Passing and its Applications pdf html PhD. Thesis

2002

Choudrey, R. and Roberts, S. Variational Mixture of Bayesian Independent Component Analysers ps.gz Neural Computation

Gao, J., Gunn, S. and Kandola, J. Adapting Kernels by Variational Approach in SVM Lecture Notes in Artificial Intelligence, Vol.2557 pp395-406 (2002)

Ueda, N. and Ghahramani, Z. Bayesian Model Search for Mixture Models Based on Optimizing Variational Bounds Neural Networks
2001

Girolami, M. A Variational Method for Learning Sparse and Overcomplete Representations abstract pdf.zip Neural Computation 13(11):2517-2532

Ghahramani, Z. and Beal, M.J. Propagation Algorithms for Variational Bayesian Learning abstract pdf ps.gz poster software NIPS 13

Minka, T.P. Using lower bounds to approximate integrals abstract ps.gz

Sato, M. On-line model selection based on the variational Bayes Neural Computation, 13(7), 1649-1681

MacKay, D. J. C. A Problem with Variational Free Energy Minimization website First published Sat 23/6/01

2000

Attias, H A Variational Bayesian framework for graphical models ps NIPS 12

Bishop, C.M. and Tipping, M.E. Variational Relevance Vector Machines citeseer
Bishop, C.M. and Winn, J.M. Non-linear Bayesian Image Modelling Proc. European Conf. on Computer Vision. ECCV
Choudrey, R., Penny, W.D. and Roberts, S.J. An Ensemble Learning Approach to Independent Component Analysis ps IEEE Workshop on Neural Networks for Signal Processing, Sydney Australia, Dec. 2000

Ghahramani, Z. and Beal, M.J. Variational Inference for Bayesian Mixtures of Factor Analysers abstract pdf ps.gz poster software NIPS 12:449-455

Ghahramani, Z. and Beal, M.J. Graphical Models and Variational Methods abstract pdf ps.gz Advanced Mean Field methods - Theory and Practice, eds. D. Saad and M. Opper, MIT Press. jacket

Jaakkola, T. and Jordan, M.I. Bayesian parameter estimation via variational methods ps ps.gz Statistics and Computing, 10:25--37, 2000

Jaakkola, T. Tutorial on variational approximation methods ps.gz ps Advanced Mean Field methods - Theory and Practice, eds. D. Saad and M. Opper, MIT Press. jacket

Lappalainen, H. and Miskin, J.W. Ensemble Learning ps.gz Advances in Independent Component Analysis (Ed. by Girolami, M). Springer-Verlag Scientific Publishers

MacKay, D.J.C. and Gibbs, M.N. Variational Gaussian Process Classifiers citeseer IEEE Neural Networks

Miskin, J.W. Ensemble Learning for Independent Component Analysis ps.gz PhD. Thesis

Storkey, A.J. Dynamic Trees: A structured variational approach giving efficient propagation rules pdf pdf.gz djvu UAI 2000

Valpola, H. Bayesian Ensemble Learning for Nonlinear Factor Analysis ps.gz PhD. Thesis, Helsinki University of Technology, Espoo. In Acta Polytechnica Scandinavica, Mathematics and Computing Series No. 108

Valpola, H. Nonlinear Independent Component Analysis Using Ensemble Learning: Theory ps.gz Proc. of the Second International Workshop on Independent Component Analysis and Blind Signal Separation, Helsinki, Finland pp. 251-256

1999

Attias, H. Inferring parameters and structure of latent variable models by variational Bayes ps UAI 15

Bishop, C.M. Variational PCA ICANN 9
Jordan, M.I, Ghahramani, Z., Jaakkola, T.S., and Saul, L.K. An introduction to variational methods for graphical models ps.gz Machine Learning 37:183-233

Lawrence, N.D. and Azzouzi, M. A Variational Bayesian Committee of Neural Networks citeseer

1998 & before

Barber, D. and Bishop, C. M. (1998) Ensemble learning for multi-layer networks djvu NIPS 10

MacKay, D.J.C. (1997) Ensemble Learning for Hidden Markov Models abstract pdf ps.gz Unpublished manuscript

Waterhouse, S., MacKay, D.J.C. and Robinson, T. (1996) Bayesian methods for Mixtures of Experts djvu NIPS 8

MacKay, D.J.C. (1995) Developments in Probabilistic Modelling with Neural Networks -- Ensemble Learning ps and pdf In Kappen, B. and Gielen, S. (eds) Neural Networks: Artificial Intelligence and Industrial Applications. Proceedings of the 3rd Annual Symposium on Neural Networks, Nijmegen, Netherlands, 14-15 September 1995

MacKay, D.J.C. (1995) Ensemble Learning and Evidence Maximisation abstract pdf ps.gz Unpublished manuscript

Hinton, G.E. and van Camp, D. (1993) Keeping Neural Networks Simple by Minimizing the Description Length of the Weights ps COLT 6

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2003
Beal, M.J.	Variational Algorithms for Approximate Bayesian Inference	pdf html	PhD. Thesis
Beal, M.J. and Ghahramani, Z.	The Variational Bayesian EM Algorithm for Incomplete Data: with Application to Scoring Graphical Model Structures	pdf ps.gz	Bayesian Statistics 7, Oxford University Press, 2003.
Watanabe, S., Minami, Y., Nakamura, A. and Ueda, N.	Application of Variational Bayesian Approach to Speech Recognition	ps.gz pdf	NIPS 15
Winn, J.	Variational Message Passing and its Applications	pdf html	PhD. Thesis
2002
Choudrey, R. and Roberts, S.	Variational Mixture of Bayesian Independent Component Analysers	ps.gz	Neural Computation
Gao, J., Gunn, S. and Kandola, J.	Adapting Kernels by Variational Approach in SVM		Lecture Notes in Artificial Intelligence, Vol.2557 pp395-406 (2002)
Ueda, N. and Ghahramani, Z.	Bayesian Model Search for Mixture Models Based on Optimizing Variational Bounds		Neural Networks
2001
Girolami, M.	A Variational Method for Learning Sparse and Overcomplete Representations	abstract pdf.zip	Neural Computation 13(11):2517-2532
Ghahramani, Z. and Beal, M.J.	Propagation Algorithms for Variational Bayesian Learning	abstract pdf ps.gz poster software	NIPS 13
Minka, T.P.	Using lower bounds to approximate integrals	abstract ps.gz
Sato, M.	On-line model selection based on the variational Bayes		Neural Computation, 13(7), 1649-1681
MacKay, D. J. C.	A Problem with Variational Free Energy Minimization	website	First published Sat 23/6/01
2000
Attias, H	A Variational Bayesian framework for graphical models	ps	NIPS 12
Bishop, C.M. and Tipping, M.E.	Variational Relevance Vector Machines	citeseer
Bishop, C.M. and Winn, J.M.	Non-linear Bayesian Image Modelling		Proc. European Conf. on Computer Vision. ECCV
Choudrey, R., Penny, W.D. and Roberts, S.J.	An Ensemble Learning Approach to Independent Component Analysis	ps	IEEE Workshop on Neural Networks for Signal Processing, Sydney Australia, Dec. 2000
Ghahramani, Z. and Beal, M.J.	Variational Inference for Bayesian Mixtures of Factor Analysers	abstract pdf ps.gz poster software	NIPS 12:449-455
Ghahramani, Z. and Beal, M.J.	Graphical Models and Variational Methods	abstract pdf ps.gz	Advanced Mean Field methods - Theory and Practice, eds. D. Saad and M. Opper, MIT Press. jacket
Jaakkola, T. and Jordan, M.I.	Bayesian parameter estimation via variational methods	ps ps.gz	Statistics and Computing, 10:25--37, 2000
Jaakkola, T.	Tutorial on variational approximation methods	ps.gz ps	Advanced Mean Field methods - Theory and Practice, eds. D. Saad and M. Opper, MIT Press. jacket
Lappalainen, H. and Miskin, J.W.	Ensemble Learning	ps.gz	Advances in Independent Component Analysis (Ed. by Girolami, M). Springer-Verlag Scientific Publishers
MacKay, D.J.C. and Gibbs, M.N.	Variational Gaussian Process Classifiers	citeseer	IEEE Neural Networks
Miskin, J.W.	Ensemble Learning for Independent Component Analysis	ps.gz	PhD. Thesis
Storkey, A.J.	Dynamic Trees: A structured variational approach giving efficient propagation rules	pdf pdf.gz djvu	UAI 2000
Valpola, H.	Bayesian Ensemble Learning for Nonlinear Factor Analysis	ps.gz	PhD. Thesis, Helsinki University of Technology, Espoo. In Acta Polytechnica Scandinavica, Mathematics and Computing Series No. 108
Valpola, H.	Nonlinear Independent Component Analysis Using Ensemble Learning: Theory	ps.gz	Proc. of the Second International Workshop on Independent Component Analysis and Blind Signal Separation, Helsinki, Finland pp. 251-256
1999
Attias, H.	Inferring parameters and structure of latent variable models by variational Bayes	ps	UAI 15
Bishop, C.M.	Variational PCA		ICANN 9
Jordan, M.I, Ghahramani, Z., Jaakkola, T.S., and Saul, L.K.	An introduction to variational methods for graphical models	ps.gz	Machine Learning 37:183-233
Lawrence, N.D. and Azzouzi, M.	A Variational Bayesian Committee of Neural Networks	citeseer
1998 & before
Barber, D. and Bishop, C. M. (1998)	Ensemble learning for multi-layer networks	djvu	NIPS 10
MacKay, D.J.C. (1997)	Ensemble Learning for Hidden Markov Models	abstract pdf ps.gz	Unpublished manuscript
Waterhouse, S., MacKay, D.J.C. and Robinson, T. (1996)	Bayesian methods for Mixtures of Experts	djvu	NIPS 8
MacKay, D.J.C. (1995)	Developments in Probabilistic Modelling with Neural Networks -- Ensemble Learning	ps and pdf	In Kappen, B. and Gielen, S. (eds) Neural Networks: Artificial Intelligence and Industrial Applications. Proceedings of the 3rd Annual Symposium on Neural Networks, Nijmegen, Netherlands, 14-15 September 1995
MacKay, D.J.C. (1995)	Ensemble Learning and Evidence Maximisation	abstract pdf ps.gz	Unpublished manuscript
Hinton, G.E. and van Camp, D. (1993)	Keeping Neural Networks Simple by Minimizing the Description Length of the Weights	ps	COLT 6