Albert JH, Chib S. (1993). Bayesian analysis of binary and polychotomous response data J Am Stat Assoc. 88

Beal M. (2003). Variational algorithms for approximate bayesian inference Unpublished doctoral dissertation.

Csato L, Opper M, Winther O, Fokue E, Schottky B. (2000). Efficient approaches to gaussian process classification Advances in neural information processing systems. 12

Csató L, Opper M. (2002). Sparse on-line gaussian processes. Neural computation. 14 [PubMed]

Duan K, Keerthi S. (2005). Which is the best multi-class SVM method? An empirical study Proceedings of the Sixth International Workshop on Multiple Classifier Systems.

Ghahramani Z, Chu W. (2005). Gaussian processes for ordinal regression J Mach Learn Res. 6

Ghahramani Z, Jordan MI, Jaakkola TS, Saul LK. (1999). An introduction to variational methods for graphical models Mach Learn. 37

Ghahramani Z, Minka TP, Qi Y, Picard RW. (2004). Predictive automatic relevance determination by expectation propagation Proceedings of the Twenty-First International Conference on Machine Learning.

Gibbs MN, MacKay DC. (2000). Variational Gaussian process classifiers. IEEE transactions on neural networks. 11 [PubMed]

Girolami M, Rogers S. (2005). Hierarchic Bayesian models for kernel learning Proceedings of the 22nd International Conference on Machine Learning.

Herbrich R, Lawrence ND, Seeger M. (2003). Fast sparse gaussian process methods: The informative vector machine Advances in neural information processing systems. 15

Jordan MI, Lawrence ND, Platt JC. (2005). Extensions of the informative vector machine Proceedings of the Sheffield Machine Learning Workshop.

Jordan MI, Seeger M. (2004). Sparse gaussian Process classification with multiple classes Tech. Rep. 661.

Kim HC. (2005). Bayesian and ensemble kernel classifiers Unpublished doctoral dissertation (Available online at http:--home.postech.ac.kr-grass-publication-).

Kuss M, Rasmussen CE. (2005). Assessing approximate inference for binary gaussian process classification J Mach Learn Res. 6

Lawrence ND, Milo M, Niranjan M, Rashbass P, Soullier S. (2004). Reducing the variability in cDNA microarray image processing by Bayesian inference. Bioinformatics (Oxford, England). 20 [PubMed]

MacKay DJ. (2003). Information Theory, Inference and Learning Algorithms.

Minka TP. (2001). A family of algorithms for approximate Bayesian inference Unpublished doctoral dissertation.

Neal R. (1998). Regression and classification using gaussian process priors Bayesian statistics. 6

Opper M, Winther O. (2000). Gaussian processes for classification: mean-field algorithms. Neural computation. 12 [PubMed]

Seeger M. (2000). Bayesian model selection for support vector machines, gaussian processes and other kernel classifiers Advances in neural information processing systems. 12

Shawe-taylor J, Cristianini N, Platt JC. (2000). Large margin DAGs for multi-class classification Advances in neural information processing systems. 12

Tipping M. (2001). Sparse Bayesian learning and the relevance vector machine J Mach Learn Res. 1

Wang B, Titterington DM. (2004). Convergence and asymptotic normality of variational Bayesian approximations for exponential family models with missing values Tech. Rep. No. 04-02.

Williams CKI, Barber D. (1998). Bayesian classification with gaussian processes IEEE Trans Patt Anal Mach Intel. 20

Williams CKI, Lawrence ND, Seeger M. (2003). Fast forward selection to speed up sparse gaussian process regression Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics.

Williams CKI, Rasmussen CE. (1996). Gaussian processes for regression Advances in neural processing systems. 8

Williams CKI, Seeger M. (2001). Using the Nystrom method to speed up kernel machines Advances in neural information processing systems. 13

Winther O, Quinonero-Candela J. (2003). Incremental gaussian processes Advances in neural information processing systems. 15