Professor Mike Titterington's Research Page

Research Interests

Research
Areas

	Statistical Analysis of Mixture distributions
	Analysis of Incomplete data
	Latent Structure Analysis
	Neural Networks, Pattern Recognition and Machine Learning
	Smoothing and Non Parametric Statistics
	Medical Statistics and Optimum Design of Experiments

Some Recent Publications

2009

McGrory, C.A. and Titterington, D.M. Variational Bayesian analysis for hidden Markov random fields. Aust. New Zeal. J. Statist., to appear.
Wang, B. and Titterington, D.M. Variational Bayesian inference for partially observed stochastic dynamical systems. J. Phys. Conf. Series, to appear.

2008

Xue, J.-H. and Titterington, D.M. Interpretation of hybrid generative/discriminative algorithms. Neurocomputing, to appear.
McGrory, C.A., Titterington, D.M., Reeves, R. and Pettitt, A.N Variational Bayes for estimating the parameters of a hidden Potts model. Statistics and Computing, to appear.
Xue, J.-H. and Titterington, D.M. Do unbalanced data have a negative effect on LDA? Pattern Recognition, 41, 1575--1588.
Xue, J.-H. and Titterington, D.M. Short note on two output-dependent hidden Markov models. Pattern Recognition Letters, 29, 1424--1426.

2007

Tanaka, K. and Titterington, D.M. Statistical trajectory of approximate EM algorithm for probabilistic image processing. Journal of Physics A: Mathematical and Theoretical, 40, 11285--11300.
Dolia, A.N., Harris, C.J., Shawe-Taylor, J.S. and Titterington, D.M. Kernel ellipsoidal trimming. Comp. Statist. Data Anal., 52, 309--324.
McGrory, C.A. and Titterington, D.M. Variational approximations in Bayesian model selection for finite mixture distributions. Comp. Statist. Data Anal., 51, 5352--5367.
Shi, J.Q., Wang, B., Murray-Smith, R. and Titterington, D.M. Gaussian process functional regression modelling for batch data. Biometrics, 63, 714--723.

2006

Wang, B. and Titterington, D.M. Convergence properties of a general algorithm for calculating variational Bayesian estimates for a normal mixture model. Bayesian Analysis, 1, 625--650.
Celeux, G., Forbes, F., Robert, C.P. and Titterington, D.M. Deviance Information Criteria for missing data models (with discussion). Bayesian Analysis, 1, 651--706.
Titterington, D.M. Some aspects of latent structure analysis. In Subspace, Latent Structure and Feature Selection, Lecture Notes in Computer Science Vol. 3940, Ed. C. Saunders, M. Grobelnik, S. Gunn and J. Shawe-Taylor, pp. 69--83. Springer-Verlag.

Titterington, D.M. Some aspects of statistical image modelling and restoration. In Statistical Problems in Particle Physics, Astrophysics and Cosmology: Proceedings of PHYSTAT05. Ed. L. Lyons and M.K. �el, pp. 255--266. Imperial College Press.

Dolia, A.N., De Bie, T., Harris, C.J., Shawe-Taylor, J. and Titterington, D.M. The minimum volume covering ellipsoid estimation in kernel-defined feature spaces. ECML 2006, 630--637.

2005

Shi, J.Q., Murray-Smith, R., Pearlmutter, B.A. and Titterington, D.M. Filtered Gaussian processes for learning with large data-sets. In Switching and Learning, Lecture Notes in Computer Science Vol. 3355, Ed. R. Murray-Smith and R. Shorten, pp. 128--139. Springer-Verlag.

Shi, J.Q., Murray-Smith, R. and Titterington, D.M. Hierarchical Gaussian process mixtures for regression. Statist. Comp., 15, 31--41.

Wang, B. and Titterington, D.M. Variational Bayes estimation of mixing coefficients. In Deterministic and Statistical Methods in Machine Learning, Lecture Notes in Artificial Intelligence Vol. 3635, Ed. J. Winkler, M. Niranjan and N. Lawrence, pp. 281--295. Springer-Verlag.
Wang, B. and Titterington, D.M. Inadequacy of interval estimates corresponding to variational Bayesian approximations. In Proc. 10th Int. Workshop Artific. Intell. Statist., Ed. R.G. Cowell and Z. Ghahramani, pp. 373--380.

2004

Titterington, D.M. Bayesian methods for neural networks and related models. Statist. Sci., 19, 128--139.
Titterington, D.M. Statistical modeling and computation. In Applied Bayesian and Causal Inference with and without Missing Data, Ed. A. Gelman and X.-L. Meng, pp.183--188. Wiley, New York.
Tanaka, K., Shouno, H., Okada, M. and Titterington, D.M.) Accuracy of the Bethe approximation for hyperparameter estimation in probabilistic image processing. J. Phys. A, 37, 8675--8695.
Tanaka, K. and Titterington, D.M. Probabilistic image processing based on the Q-Ising model by means of the mean-field method and loopy belief propagation. In Proceedings of 17th International Conference on Pattern Recognition, Vol.2, pp.40--43. IEEE Computer Society Press.
Wang, B. and Titterington, D.M. Convergence and asymptotic normality of variational Bayesian approximations for exponential family models with missing values. In Proc. 20th Conf. Uncertainty in Artificial Intell., Ed. M. Chickering and J. Halperin, pp. 577--584. AUAI Press.
Wang, B. and Titterington, D.M. Lack of consistency of mean field and variational Bayes approximations for state space models. Neural Proc. Lett., 20, 151--170.
Tanaka, K. and Titterington, D.M. First-order phase transition and Bayesian image processing by loopy belief propagation. In Progress in Theoretical Physics 157, Ed. K. Hukushima, K. Tanaka and H. Nishimori, pp.288--291.

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