D. M. Titterington

According to our database, D. M. Titterington authored at least 46 papers between 1984 and 2016.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2016

Transdimensional sequential Monte Carlo using variational Bayes - SMCVB.

Clare A. McGrory, Anthony N. Pettitt, D. M. Titterington, Clair L. Alston, Matt Kelly

Comput. Stat. Data Anal., 2016

2011

t -Tests, F -Tests and Otsu's Methods for Image Thresholding.

Jing-Hao Xue, D. Mike Titterington

IEEE Trans. Image Process., 2011

Median-based image thresholding.

Jing-Hao Xue, D. M. Titterington

Image Vis. Comput., 2011

2010

Joint discriminative-generative modelling based on statistical tests for classification.

Jing-Hao Xue, D. Mike Titterington

Pattern Recognit. Lett., 2010

Preface.

Yee Whye Teh, D. Mike Titterington

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

On the generative-discriminative tradeoff approach: Interpretation, asymptotic efficiency and classification performance.

Jing-Hao Xue, D. M. Titterington

Comput. Stat. Data Anal., 2010

2009

Variational Bayes for estimating the parameters of a hidden Potts model.

Clare A. McGrory, D. M. Titterington, R. Reeves, Anthony N. Pettitt

Stat. Comput., 2009

Interpretation of hybrid generative/discriminative algorithms.

Jing-Hao Xue, D. Mike Titterington

Neurocomputing, 2009

2008

Short note on two output-dependent hidden Markov models.

Jing-Hao Xue, D. M. Titterington

Pattern Recognit. Lett., 2008

Do unbalanced data have a negative effect on LDA?

Jing-Hao Xue, D. Mike Titterington

Pattern Recognit., 2008

Comment on "On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes".

Jing-Hao Xue, D. M. Titterington

Neural Process. Lett., 2008

2007

Variational approximations in Bayesian model selection for finite mixture distributions.

Clare A. McGrory, D. M. Titterington

Comput. Stat. Data Anal., 2007

Kernel ellipsoidal trimming.

Alexander N. Dolia, Christopher J. Harris, John Shawe-Taylor, D. Mike Titterington

Comput. Stat. Data Anal., 2007

2006

The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces.

Alexander N. Dolia, Tijl De Bie, Christopher J. Harris, John Shawe-Taylor, D. M. Titterington

Proceedings of the Machine Learning: ECML 2006, 2006

2005

Hierarchical Gaussian process mixtures for regression.

Jian Qing Shi, Roderick Murray-Smith, D. M. Titterington

Stat. Comput., 2005

Some Aspects of Latent Structure Analysis.

D. Mike Titterington

Proceedings of the Subspace, 2005

Inadequacy of interval estimates corresponding to variational Bayesian approximations.

Bo Wang, D. M. Titterington

Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

2004

Lack of Consistency of Mean Field and Variational break Bayes Approximations for State Space Models.

Bo Wang, D. M. Titterington

Neural Process. Lett., 2004

Convergence and Asymptotic Normality of Variational Bayesian Approximations for Expon.

Bo Wang, D. M. Titterington

Proceedings of the UAI '04, 2004

Probabilistic Image Processing based on the Q-Ising Model by Means of the Mean-Field Method and Loopy Belief Propagation.

Kazuyuki Tanaka, D. M. Titterington

Proceedings of the 17th International Conference on Pattern Recognition, 2004

Variational Bayes Estimation of Mixing Coefficients.

Bo Wang, D. M. Titterington

Proceedings of the Deterministic and Statistical Methods in Machine Learning, 2004

2003

Mixtures of Factor Analysers. Bayesian Estimation and Inference by Stochastic Simulation.

Ernest Fokoué, D. M. Titterington

Mach. Learn., 2003

Loopy belief propagation and probabilistic image processing.

Kazuyuki Tanaka, J. Inoue, D. M. Titterington

Proceedings of the NNSP 2003, 2003

Filtered Gaussian Processes for Learning with Large Data-Sets.

Jian Qing Shi, Roderick Murray-Smith, D. M. Titterington, Barak A. Pearlmutter

Proceedings of the Switching and Learning in Feedback Systems, 2003

2001

Improved Particle Filters and Smoothing.

Photis Stavropoulos, D. M. Titterington

Proceedings of the Sequential Monte Carlo Methods in Practice, 2001

2000

Statistics and Neural Networks.

Jim W. Kay, D. M. Titterington

Technometrics, 2000

Improving the Mean-Field Approximation in Belief Networks Using Bahadur's Reparameterisation of the Multivariate Binary Distribution.

Keith Humphreys, D. M. Titterington

Neural Process. Lett., 2000

1999

Discussion on the paper by Friedman and Fisher.

D. M. Titterington

Stat. Comput., 1999

On perfect simulation for some mixtures of distributions.

James P. Hobert, Christian P. Robert, D. M. Titterington

Stat. Comput., 1999

Modelling magnetic material images with simultaneous autoregressions.

Changjing Shang, D. M. Titterington

Proceedings of the 1999 IEEE International Conference on Acoustics, 1999

The exploration of new methods for learning in binary Boltzmann machines.

Keith Humphreys, D. M. Titterington

Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999

1998

Reparameterization strategies for hidden Markov models and Bayesian approaches to maximum likelihood estimation.

Christian P. Robert, D. M. Titterington

Stat. Comput., 1998

Mean fields and two-dimensional Markov random fields in image analysis.

A. P. Dunmur, D. M. Titterington

Pattern Anal. Appl., 1998

1997

On the shrinkage of local linear curve estimators.

Ming-Yen Cheng, Peter Hall, D. M. Titterington

Stat. Comput., 1997

Computational Bayesian Analysis of Hidden Markov Mesh Models.

A. P. Dunmur, D. M. Titterington

IEEE Trans. Pattern Anal. Mach. Intell., 1997

1996

On a Modification to the Mean Field EM Algorithm in Factorial Learning.

A. P. Dunmur, D. M. Titterington

Proceedings of the Advances in Neural Information Processing Systems 9, 1996

1995

Beyond the binary Boltzmann machine.

Niall H. Anderson, D. M. Titterington

IEEE Trans. Neural Networks, 1995

On some Bayesian/regularization methods for image restoration.

Graeme Archer, D. M. Titterington

IEEE Trans. Image Process., 1995

1994

An Empirical Study of the Simulation of Various Models used for Images.

Alison J. Gray, Jim Kay, D. M. Titterington

IEEE Trans. Pattern Anal. Mach. Intell., 1994

1993

Bayesian Image Restoration: An Application to Edge-Preserving Surface Recovery.

Wei Qian, D. M. Titterington

IEEE Trans. Pattern Anal. Mach. Intell., 1993

1992

On the estimation of noisy binary Markov random fields.

Alison J. Gray, Jim Kay, D. M. Titterington

Pattern Recognit., 1992

1991

Pixel labelling for three-dimensional scenes based on Markov mesh models.

Wei Qian, D. M. Titterington

Signal Process., 1991

A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization.

Alan M. Thompson, John C. Brown, Jim Kay, D. M. Titterington

IEEE Trans. Pattern Anal. Mach. Intell., 1991

1989

An alternative stochastic supervisor in discriminant analysis.

D. Mike Titterington

Pattern Recognit., 1989

1987

A Re-Examination of the Distance-Weighted k-Nearest Neighbor Classification Rule.

James E. S. Macleod, Andrew Luk, D. Mike Titterington

IEEE Trans. Syst. Man Cybern., 1987

1984

Comments on "Application of the Conditional Population-Mixture Model to Image Segmentation".

D. M. Titterington

IEEE Trans. Pattern Anal. Mach. Intell., 1984