D. M. Titterington

Publication Activity (10 Years)

Years Active: 1984-2016
Publications (10 Years): 1

Top Topics

Variational Bayes

Sequential Monte Carlo

Posterior Distribution

Latent Dirichlet Allocation

Top Venues

Comput. Stat. Data Anal.

Publications

Clare A. McGrory, Anthony N. Pettitt, D. M. Titterington, Clair L. Alston, Matt Kelly
Transdimensional sequential Monte Carlo using variational Bayes - SMCVB. Comput. Stat. Data Anal. 93 (2016)
Jing-Hao Xue, D. M. Titterington
Median-based image thresholding. Image Vis. Comput. 29 (9) (2011)
Jing-Hao Xue, D. M. Titterington
On the generative-discriminative tradeoff approach: Interpretation, asymptotic efficiency and classification performance. Comput. Stat. Data Anal. 54 (2) (2010)
Clare A. McGrory, D. M. Titterington, R. Reeves, Anthony N. Pettitt
Variational Bayes for estimating the parameters of a hidden Potts model. Stat. Comput. 19 (3) (2009)
Jing-Hao Xue, D. M. Titterington
Comment on "On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes". Neural Process. Lett. 28 (3) (2008)
Jing-Hao Xue, D. M. Titterington
Short note on two output-dependent hidden Markov models. Pattern Recognit. Lett. 29 (9) (2008)
Clare A. McGrory, D. M. Titterington
Variational approximations in Bayesian model selection for finite mixture distributions. Comput. Stat. Data Anal. 51 (11) (2007)
Alexander N. Dolia, Tijl De Bie, Christopher J. Harris, John Shawe-Taylor, D. M. Titterington
The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces. ECML (2006)
Jian Qing Shi, Roderick Murray-Smith, D. M. Titterington
Hierarchical Gaussian process mixtures for regression. Stat. Comput. 15 (1) (2005)
Bo Wang, D. M. Titterington
Inadequacy of interval estimates corresponding to variational Bayesian approximations. AISTATS (2005)
Bo Wang, D. M. Titterington
Convergence and Asymptotic Normality of Variational Bayesian Approximations for Expon. UAI (2004)
Bo Wang, D. M. Titterington
Variational Bayes Estimation of Mixing Coefficients. Deterministic and Statistical Methods in Machine Learning (2004)
Kazuyuki Tanaka, D. M. Titterington
Probabilistic Image Processing based on the Q-Ising Model by Means of the Mean-Field Method and Loopy Belief Propagation. ICPR (2) (2004)
Bo Wang, D. M. Titterington
Lack of Consistency of Mean Field and Variational break Bayes Approximations for State Space Models. Neural Process. Lett. 20 (3) (2004)
Ernest Fokoué, D. M. Titterington
Mixtures of Factor Analysers. Bayesian Estimation and Inference by Stochastic Simulation. Mach. Learn. 50 (1-2) (2003)
Kazuyuki Tanaka, J. Inoue, D. M. Titterington
Loopy belief propagation and probabilistic image processing. NNSP (2003)
Jian Qing Shi, Roderick Murray-Smith, D. M. Titterington, Barak A. Pearlmutter
Filtered Gaussian Processes for Learning with Large Data-Sets. European Summer School on Multi-AgentControl (2003)
Jim W. Kay, D. M. Titterington
Statistics and Neural Networks. Technometrics 42 (4) (2000)
Keith Humphreys, D. M. Titterington
Improving the Mean-Field Approximation in Belief Networks Using Bahadur's Reparameterisation of the Multivariate Binary Distribution. Neural Process. Lett. 12 (2) (2000)
James P. Hobert, Christian P. Robert, D. M. Titterington
On perfect simulation for some mixtures of distributions. Stat. Comput. 9 (4) (1999)
D. M. Titterington
Discussion on the paper by Friedman and Fisher. Stat. Comput. 9 (2) (1999)
Keith Humphreys, D. M. Titterington
The exploration of new methods for learning in binary Boltzmann machines. AISTATS (1999)
Changjing Shang, D. M. Titterington
Modelling magnetic material images with simultaneous autoregressions. ICASSP (1999)
A. P. Dunmur, D. M. Titterington
Mean fields and two-dimensional Markov random fields in image analysis. Pattern Anal. Appl. 1 (4) (1998)
Christian P. Robert, D. M. Titterington
Reparameterization strategies for hidden Markov models and Bayesian approaches to maximum likelihood estimation. Stat. Comput. 8 (2) (1998)
A. P. Dunmur, D. M. Titterington
Computational Bayesian Analysis of Hidden Markov Mesh Models. IEEE Trans. Pattern Anal. Mach. Intell. 19 (11) (1997)
Ming-Yen Cheng, Peter Hall, D. M. Titterington
On the shrinkage of local linear curve estimators. Stat. Comput. 7 (1) (1997)
A. P. Dunmur, D. M. Titterington
On a Modification to the Mean Field EM Algorithm in Factorial Learning. NIPS (1996)
Graeme Archer, D. M. Titterington
On some Bayesian/regularization methods for image restoration. IEEE Trans. Image Process. 5 (7) (1995)
Niall H. Anderson, D. M. Titterington
Beyond the binary Boltzmann machine. IEEE Trans. Neural Networks 6 (5) (1995)
Alison J. Gray, Jim Kay, D. M. Titterington
An Empirical Study of the Simulation of Various Models used for Images. IEEE Trans. Pattern Anal. Mach. Intell. 16 (5) (1994)
Wei Qian, D. M. Titterington
Bayesian Image Restoration: An Application to Edge-Preserving Surface Recovery. IEEE Trans. Pattern Anal. Mach. Intell. 15 (7) (1993)
Alison J. Gray, Jim Kay, D. M. Titterington
On the estimation of noisy binary Markov random fields. Pattern Recognit. 25 (7) (1992)
Wei Qian, D. M. Titterington
Pixel labelling for three-dimensional scenes based on Markov mesh models. Signal Process. 22 (3) (1991)
Alan M. Thompson, John C. Brown, Jim Kay, D. M. Titterington
A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization. IEEE Trans. Pattern Anal. Mach. Intell. 13 (4) (1991)
D. M. Titterington
Comments on "Application of the Conditional Population-Mixture Model to Image Segmentation". IEEE Trans. Pattern Anal. Mach. Intell. 6 (5) (1984)