D. M. Titterington

37 papers A 3Misc 1Journal 26Unranked 6
YearRankTypeTitle / Venue / Authors
2016 J jnl
Transdimensional sequential Monte Carlo using variational Bayes - SMCVB.
Comput. Stat. Data Anal.
Clare A. McGrory, Anthony N. Pettitt, D. M. Titterington, Clair L. Alston, Matt Kelly
2011 J jnl
Median-based image thresholding.
Image Vis. Comput.
Jing-Hao Xue, D. M. Titterington
2010 J jnl
On the generative-discriminative tradeoff approach: Interpretation, asymptotic efficiency and classification performance.
Comput. Stat. Data Anal.
Jing-Hao Xue, D. M. Titterington
2009 J jnl
Variational Bayes for estimating the parameters of a hidden Potts model.
Stat. Comput.
Clare A. McGrory, D. M. Titterington, R. Reeves, Anthony N. Pettitt
2008 J jnl
Comment on "On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes".
Neural Process. Lett.
Jing-Hao Xue, D. M. Titterington
2008 J jnl
Short note on two output-dependent hidden Markov models.
Pattern Recognit. Lett.
Jing-Hao Xue, D. M. Titterington
2007 J jnl
Variational approximations in Bayesian model selection for finite mixture distributions.
Comput. Stat. Data Anal.
Clare A. McGrory, D. M. Titterington
2006 — conf
The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces.
ECML
Alexander N. Dolia, Tijl De Bie, Christopher J. Harris, John Shawe-Taylor, D. M. Titterington
2005 J jnl
Hierarchical Gaussian process mixtures for regression.
Stat. Comput.
Jian Qing Shi, Roderick Murray-Smith, D. M. Titterington
2005 A conf
Inadequacy of interval estimates corresponding to variational Bayesian approximations.
AISTATS
Bo Wang, D. M. Titterington
2004 A conf
Convergence and Asymptotic Normality of Variational Bayesian Approximations for Expon.
UAI
Bo Wang, D. M. Titterington
2004 J jnl
Lack of Consistency of Mean Field and Variational break Bayes Approximations for State Space Models.
Neural Process. Lett.
Bo Wang, D. M. Titterington
2004 — conf
Probabilistic Image Processing based on the Q-Ising Model by Means of the Mean-Field Method and Loopy Belief Propagation.
ICPR (2)
Kazuyuki Tanaka, D. M. Titterington
2004 — conf
Variational Bayes Estimation of Mixing Coefficients.
Deterministic and Statistical Methods in Machine Learning
Bo Wang, D. M. Titterington
2003 — conf
Filtered Gaussian Processes for Learning with Large Data-Sets.
European Summer School on Multi-AgentControl
Jian Qing Shi, Roderick Murray-Smith, D. M. Titterington, Barak A. Pearlmutter
2003 — conf
Loopy belief propagation and probabilistic image processing.
NNSP
Kazuyuki Tanaka, J. Inoue, D. M. Titterington
2003 J jnl
Mixtures of Factor Analysers. Bayesian Estimation and Inference by Stochastic Simulation.
Mach. Learn.
Ernest Fokoué, D. M. Titterington
2001 — ch.
Improved Particle Filters and Smoothing.
Sequential Monte Carlo Methods in Practice
Photis Stavropoulos, D. M. Titterington
2000 J jnl
Improving the Mean-Field Approximation in Belief Networks Using Bahadur's Reparameterisation of the Multivariate Binary Distribution.
Neural Process. Lett.
Keith Humphreys, D. M. Titterington
2000 J jnl
Statistics and Neural Networks.
Technometrics
Jim W. Kay, D. M. Titterington
1999 J jnl
Discussion on the paper by Friedman and Fisher.
Stat. Comput.
D. M. Titterington
1999 Misc conf
Modelling magnetic material images with simultaneous autoregressions.
ICASSP
Changjing Shang, D. M. Titterington
1999 J jnl
On perfect simulation for some mixtures of distributions.
Stat. Comput.
James P. Hobert, Christian P. Robert, D. M. Titterington
1999 A conf
The exploration of new methods for learning in binary Boltzmann machines.
AISTATS
Keith Humphreys, D. M. Titterington
1998 J jnl
Mean fields and two-dimensional Markov random fields in image analysis.
Pattern Anal. Appl.
A. P. Dunmur, D. M. Titterington
1998 J jnl
Reparameterization strategies for hidden Markov models and Bayesian approaches to maximum likelihood estimation.
Stat. Comput.
Christian P. Robert, D. M. Titterington
1997 J jnl
Computational Bayesian Analysis of Hidden Markov Mesh Models.
IEEE Trans. Pattern Anal. Mach. Intell.
A. P. Dunmur, D. M. Titterington
1997 J jnl
On the shrinkage of local linear curve estimators.
Stat. Comput.
Ming-Yen Cheng, Peter Hall, D. M. Titterington
1996 — conf
On a Modification to the Mean Field EM Algorithm in Factorial Learning.
NIPS
A. P. Dunmur, D. M. Titterington
1995 J jnl
Beyond the binary Boltzmann machine.
IEEE Trans. Neural Networks
Niall H. Anderson, D. M. Titterington
1995 J jnl
On some Bayesian/regularization methods for image restoration.
IEEE Trans. Image Process.
Graeme Archer, D. M. Titterington
1994 J jnl
An Empirical Study of the Simulation of Various Models used for Images.
IEEE Trans. Pattern Anal. Mach. Intell.
Alison J. Gray, Jim Kay, D. M. Titterington
1993 J jnl
Bayesian Image Restoration: An Application to Edge-Preserving Surface Recovery.
IEEE Trans. Pattern Anal. Mach. Intell.
Wei Qian, D. M. Titterington
1992 J jnl
On the estimation of noisy binary Markov random fields.
Pattern Recognit.
Alison J. Gray, Jim Kay, D. M. Titterington
1991 J jnl
A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization.
IEEE Trans. Pattern Anal. Mach. Intell.
Alan M. Thompson, John C. Brown, Jim Kay, D. M. Titterington
1991 J jnl
Pixel labelling for three-dimensional scenes based on Markov mesh models.
Signal Process.
Wei Qian, D. M. Titterington
1984 J jnl
Comments on "Application of the Conditional Population-Mixture Model to Image Segmentation".
IEEE Trans. Pattern Anal. Mach. Intell.
D. M. Titterington