Comments on "Application of the Conditional Population-Mixture Model to Image Segmentation". - D. M. Titterington (IEEE Trans. Pattern Anal. Mach. Intell., 1984)

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)

An alternative stochastic supervisor in discriminant analysis. - D. Mike Titterington (Pattern Recognit., 1989)

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)

Pixel labelling for three-dimensional scenes based on Markov mesh models. - Wei Qian; D. M. Titterington (Signal Process., 1991)

On the estimation of noisy binary Markov random fields. - Alison J. Gray; Jim Kay; D. M. Titterington (Pattern Recognit., 1992)

Bayesian Image Restoration: An Application to Edge-Preserving Surface Recovery. - Wei Qian; D. M. Titterington (IEEE Trans. Pattern Anal. Mach. Intell., 1993)

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)

On some Bayesian/regularization methods for image restoration. - Graeme Archer; D. M. Titterington (IEEE Trans. Image Process., 1995)

Beyond the binary Boltzmann machine. - Niall H. Anderson; D. M. Titterington (IEEE Trans. Neural Networks, 1995)

On a Modification to the Mean Field EM Algorithm in Factorial Learning. - A. P. Dunmur; D. M. Titterington (NIPS, 1996)

Computational Bayesian Analysis of Hidden Markov Mesh Models. - A. P. Dunmur; D. M. Titterington (IEEE Trans. Pattern Anal. Mach. Intell., 1997)

On the shrinkage of local linear curve estimators. - Ming-Yen Cheng; Peter Hall; D. M. Titterington (Stat. Comput., 1997)

Mean fields and two-dimensional Markov random fields in image analysis. - A. P. Dunmur; D. M. Titterington (Pattern Anal. Appl., 1998)

Reparameterization strategies for hidden Markov models and Bayesian approaches to maximum likelihood estimation. - Christian P. Robert; D. M. Titterington (Stat. Comput., 1998)

The exploration of new methods for learning in binary Boltzmann machines. - Keith Humphreys; D. M. Titterington (AISTATS, 1999)

Modelling magnetic material images with simultaneous autoregressions. - Changjing Shang; D. M. Titterington (ICASSP, 1999)

On perfect simulation for some mixtures of distributions. - James P. Hobert; Christian P. Robert; D. M. Titterington (Stat. Comput., 1999)

Discussion on the paper by Friedman and Fisher. - D. M. Titterington (Stat. Comput., 1999)

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)

Statistics and Neural Networks. - Jim W. Kay; D. M. Titterington (Technometrics, 2000)

Improved Particle Filters and Smoothing. - Photis Stavropoulos; D. M. Titterington (Sequential Monte Carlo Methods in Practice, 2001)

Filtered Gaussian Processes for Learning with Large Data-Sets. - Jian Qing Shi; Roderick Murray-Smith; D. M. Titterington; Barak A. Pearlmutter (European Summer School on Multi-AgentControl, 2003)

Loopy belief propagation and probabilistic image processing. - Kazuyuki Tanaka; J. Inoue; D. M. Titterington (NNSP, 2003)

Mixtures of Factor Analysers. Bayesian Estimation and Inference by Stochastic Simulation. - Ernest Fokoué; D. M. Titterington (Mach. Learn., 2003)

Variational Bayes Estimation of Mixing Coefficients. - Bo Wang 002; D. M. Titterington (Deterministic and Statistical Methods in Machine Learning, 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 (ICPR (2), 2004)

Convergence and Asymptotic Normality of Variational Bayesian Approximations for Expon. - Bo Wang 002; D. M. Titterington (UAI, 2004)

Lack of Consistency of Mean Field and Variational break Bayes Approximations for State Space Models. - Bo Wang 002; D. M. Titterington (Neural Process. Lett., 2004)

Inadequacy of interval estimates corresponding to variational Bayesian approximations. - Bo Wang 002; D. M. Titterington (AISTATS, 2005)

Some Aspects of Latent Structure Analysis. - D. Mike Titterington (SLSFS, 2005)

Hierarchical Gaussian process mixtures for regression. - Jian Qing Shi; Roderick Murray-Smith; D. M. Titterington (Stat. Comput., 2005)

The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces. - Alexander N. Dolia; Tijl De Bie; Christopher J. Harris 001; John Shawe-Taylor; D. M. Titterington (ECML, 2006)

Kernel ellipsoidal trimming. - Alexander N. Dolia; Christopher J. Harris 001; John Shawe-Taylor; D. Mike Titterington (Comput. Stat. Data Anal., 2007)

Variational approximations in Bayesian model selection for finite mixture distributions. - Clare A. McGrory; D. M. Titterington (Comput. Stat. Data Anal., 2007)

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)

Do unbalanced data have a negative effect on LDA? - Jing-Hao Xue; D. Mike Titterington (Pattern Recognit., 2008)

Short note on two output-dependent hidden Markov models. - Jing-Hao Xue; D. M. Titterington (Pattern Recognit. Lett., 2008)

Interpretation of hybrid generative/discriminative algorithms. - Jing-Hao Xue; D. Mike Titterington (Neurocomputing, 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)

On the generative-discriminative tradeoff approach: Interpretation, asymptotic efficiency and classification performance. - Jing-Hao Xue; D. M. Titterington (Comput. Stat. Data Anal., 2010)

Preface. - Yee Whye Teh; D. Mike Titterington (AISTATS, 2010)

Joint discriminative-generative modelling based on statistical tests for classification. - Jing-Hao Xue; D. Mike Titterington (Pattern Recognit. Lett., 2010)

Median-based image thresholding. - Jing-Hao Xue; D. M. Titterington (Image Vis. Comput., 2011)

t -Tests, F -Tests and Otsu's Methods for Image Thresholding. - Jing-Hao Xue; D. Mike Titterington (IEEE Trans. Image Process., 2011)

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)