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ORCIDD. M. Titterington
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2010 – 2019
- 2016
[j33]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: 246-254 (2016)- 2011
- [j32]Jing-Hao Xue, D. M. Titterington:
Median-based image thresholding. Image Vis. Comput. 29(9): 631-637 (2011) - [j31]Jing-Hao Xue, D. Mike Titterington:
t -Tests, F -Tests and Otsu's Methods for Image Thresholding. IEEE Trans. Image Process. 20(8): 2392-2396 (2011) - 2010
- [j30]Jing-Hao Xue, D. M. Titterington:
On the generative-discriminative tradeoff approach: Interpretation, asymptotic efficiency and classification performance. Comput. Stat. Data Anal. 54(2): 438-451 (2010) - [j29]Jing-Hao Xue, D. Mike Titterington:
Joint discriminative-generative modelling based on statistical tests for classification. Pattern Recognit. Lett. 31(9): 1048-1055 (2010) - [c12]Yee Whye Teh, D. Mike Titterington:
Preface. AISTATS 2010 - [e1]Yee Whye Teh, D. Mike Titterington:
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2010, Chia Laguna Resort, Sardinia, Italy, May 13-15, 2010. JMLR Proceedings 9, JMLR.org 2010 [contents]
2000 – 2009
- 2009
- [j28]Jing-Hao Xue, D. Mike Titterington:
Interpretation of hybrid generative/discriminative algorithms. Neurocomputing 72(7-9): 1648-1655 (2009) - [j27]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): 329-340 (2009) - 2008
- [j26]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): 169-187 (2008) - [j25]Jing-Hao Xue, D. Mike Titterington:
Do unbalanced data have a negative effect on LDA? Pattern Recognit. 41(5): 1558-1571 (2008) - [j24]Jing-Hao Xue, D. M. Titterington:
Short note on two output-dependent hidden Markov models. Pattern Recognit. Lett. 29(9): 1424-1426 (2008) - 2007
- [j23]Clare A. McGrory, D. M. Titterington:
Variational approximations in Bayesian model selection for finite mixture distributions. Comput. Stat. Data Anal. 51(11): 5352-5367 (2007) - [j22]Alexander N. Dolia, Christopher J. Harris, John Shawe-Taylor, D. Mike Titterington:
Kernel ellipsoidal trimming. Comput. Stat. Data Anal. 52(1): 309-324 (2007) - 2006
- [c11]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: 630-637 - 2005
- [j21]Jian Qing Shi, Roderick Murray-Smith, D. M. Titterington:
Hierarchical Gaussian process mixtures for regression. Stat. Comput. 15(1): 31-41 (2005) - [c10]Bo Wang, D. M. Titterington:
Inadequacy of interval estimates corresponding to variational Bayesian approximations. AISTATS 2005: 373-380 - [c9]D. Mike Titterington:
Some Aspects of Latent Structure Analysis. SLSFS 2005: 69-83 - 2004
- [j20]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): 151-170 (2004) - [c8]Bo Wang, D. M. Titterington:
Variational Bayes Estimation of Mixing Coefficients. Deterministic and Statistical Methods in Machine Learning 2004: 281-295 - [c7]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: 40-43 - [c6]Bo Wang, D. M. Titterington:
Convergence and Asymptotic Normality of Variational Bayesian Approximations for Expon. UAI 2004: 577-584 - 2003
- [j19]Ernest Fokoué, D. M. Titterington:
Mixtures of Factor Analysers. Bayesian Estimation and Inference by Stochastic Simulation. Mach. Learn. 50(1-2): 73-94 (2003) - [c5]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: 128-139 - [c4]Kazuyuki Tanaka, J. Inoue, D. M. Titterington:
Loopy belief propagation and probabilistic image processing. NNSP 2003: 329-338 - 2001
- [p1]Photis Stavropoulos, D. M. Titterington:
Improved Particle Filters and Smoothing. Sequential Monte Carlo Methods in Practice 2001: 295-317 - 2000
- [j18]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): 183-197 (2000) - [j17]Jim W. Kay, D. M. Titterington:
Statistics and Neural Networks. Technometrics 42(4): 443-444 (2000)
1990 – 1999
- 1999
- [j16]D. M. Titterington:
Discussion on the paper by Friedman and Fisher. Stat. Comput. 9(2): 148-149 (1999) - [j15]James P. Hobert, Christian P. Robert, D. M. Titterington:
On perfect simulation for some mixtures of distributions. Stat. Comput. 9(4): 287-298 (1999) - [c3]Keith Humphreys, D. M. Titterington:
The exploration of new methods for learning in binary Boltzmann machines. AISTATS 1999 - [c2]Changjing Shang, D. M. Titterington:
Modelling magnetic material images with simultaneous autoregressions. ICASSP 1999: 3505-3508 - 1998
- [j14]A. P. Dunmur, D. M. Titterington:
Mean fields and two-dimensional Markov random fields in image analysis. Pattern Anal. Appl. 1(4): 248-260 (1998) - [j13]Christian P. Robert, D. M. Titterington:
Reparameterization strategies for hidden Markov models and Bayesian approaches to maximum likelihood estimation. Stat. Comput. 8(2): 145-158 (1998) - 1997
- [j12]A. P. Dunmur, D. M. Titterington:
Computational Bayesian Analysis of Hidden Markov Mesh Models. IEEE Trans. Pattern Anal. Mach. Intell. 19(11): 1296-1300 (1997) - [j11]Ming-Yen Cheng, Peter Hall, D. M. Titterington:
On the shrinkage of local linear curve estimators. Stat. Comput. 7(1): 11-17 (1997) - 1996
- [c1]A. P. Dunmur, D. M. Titterington:
On a Modification to the Mean Field EM Algorithm in Factorial Learning. NIPS 1996: 431-437 - 1995
- [j10]Graeme Archer, D. M. Titterington:
On some Bayesian/regularization methods for image restoration. IEEE Trans. Image Process. 5(7): 989-995 (1995) - [j9]Niall H. Anderson, D. M. Titterington:
Beyond the binary Boltzmann machine. IEEE Trans. Neural Networks 6(5): 1229-1236 (1995) - 1994
- [j8]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): 507-513 (1994) - 1993
- [j7]Wei Qian, D. M. Titterington:
Bayesian Image Restoration: An Application to Edge-Preserving Surface Recovery. IEEE Trans. Pattern Anal. Mach. Intell. 15(7): 748-752 (1993) - 1992
- [j6]Alison J. Gray, Jim Kay, D. M. Titterington:
On the estimation of noisy binary Markov random fields. Pattern Recognit. 25(7): 749-768 (1992) - 1991
- [j5]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): 326-339 (1991) - [j4]Wei Qian, D. M. Titterington:
Pixel labelling for three-dimensional scenes based on Markov mesh models. Signal Process. 22(3): 313-328 (1991)
1980 – 1989
- 1989
- [j3]D. Mike Titterington:
An alternative stochastic supervisor in discriminant analysis. Pattern Recognit. 22(1): 91-95 (1989) - 1987
- [j2]James E. S. Macleod, Andrew Luk, D. Mike Titterington:
A Re-Examination of the Distance-Weighted k-Nearest Neighbor Classification Rule. IEEE Trans. Syst. Man Cybern. 17(4): 689-696 (1987) - 1984
- [j1]D. M. Titterington:
Comments on "Application of the Conditional Population-Mixture Model to Image Segmentation". IEEE Trans. Pattern Anal. Mach. Intell. 6(5): 656-658 (1984)
Coauthor Index
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