Professor Michael Titterington

  • Honorary Senior Research Fellow (School of Mathematics & Statistics)

Research interests

Research Interests

My research interests include optimal design, discriminant analysis and various aspects of the analysis of incomplete data, including problems concerning mixture distributions, hidden Markov chains and hidden Markov random fields. Other areas of interest include density estimation, statistical image analysis and approximation-based methods for Bayesian analysis. Several of these topics are at the interface between statistics, neural networks and machine learning.

Research Groups


Publications

List by: Type | Date

Jump to: 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 1999 | 1997 | 1994 | 1992 | 1991 | 1990 | 1989 | 1986 | 1985
Number of items: 55.

2011

Xue, J.H. and Titterington, D.M. (2011) Median-based image thresholding. Image and Vision Computing, 29(9), pp. 631-637. (doi:10.1016/j.imavis.2011.06.003)

Xue, J.H. and Titterington, D.M. (2011) The p-folded cumulative distribution function and the mean absolute deviation from the p-quantile. Statistics and Probability Letters, 81(8), pp. 1179-1182. (doi:10.1016/j.spl.2011.03.014)

Xue, J.H. and Titterington, D.M. (2011) t-tests, F-tests and Otsu's methods for image thresholding. IEEE Transactions on Image Processing, 20(8), pp. 2392-2396. (doi:10.1109/TIP.2011.2114358)

2010

Cole, D.J., Morgan, B.J.T. and Titterington, D.M. (2010) Determining the parametric structure of models. Mathematical Biosciences, 228(1), pp. 16-30. (doi:10.1016/j.mbs.2010.08.004)

Xue, J. H. and Titterington, D. M. (2010) Joint discriminative-generative modelling based on statistical tests for classification. Pattern Recognition Letters, 31(9), pp. 1048-1055. (doi:10.1016/j.patrec.2010.01.015)

Xue, J. H. and Titterington, D. M. (2010) On the generative-discriminative tradeoff approach: Interpretation, asymptotic efficiency and classification performance. Computational Statistics and Data Analysis, 54(2), pp. 438-451. (doi:10.1016/j.csda.2009.09.011)

2009

Hall, P., Titterington, D. M. and Xue, J. H. (2009) Median-Based Classifiers for High-Dimensional Data. Journal of the American Statistical Association, 104(488), pp. 1597-1608. (doi:10.1198/jasa.2009.tm08107)

Johnstone, I.M. and Titterington, D.M. (2009) Statistical challenges of high-dimensional data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367(1906), pp. 4237-4253. (doi:10.1098/rsta.2009.0159)

Hall, P., Titterington, D.M. and Xue, J. (2009) Tilting methods for assessing the influence of components in a classifier. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(4), pp. 783-803. (doi:10.1111/j.1467-9868.2009.00701.x)

McGrory, C.A., Titterington, D.M., Reeves, R. and Pettitt, A.N. (2009) Variational Bayes for estimating the parameters of a hidden Potts model. Statistics and Computing, 19(3), pp. 329-340. (doi:10.1007/s11222-008-9095-6)

Tanaka, K., Morin, N., Yasuda, M. and Titterington, D.M. (2009) Probabilistic Image Processing by Extended Gauss-Markov Random Fields. In: IEEE/SP 15th Workshop on Statistical Signal Processing, 2009, Cardiff, August 31st to September 3rd, 2009, pp. 618-621. (doi:10.1109/SSP.2009.5278499)

McGrory, C. A. and Titterington, D. M. (2009) Variational Bayesian Analysis for Hidden Markov Models. Australian and New Zealand Journal of Statistics, 51(2), pp. 227-244. (doi:10.1111/j.1467-842X.2009.00543.x)

Cucala, L., Marin, J. M., Robert, C. P. and Titterington, D.M. (2009) A Bayesian Reassessment of Nearest-Neighbor Classification. Journal of the American Statistical Association, 104(485), pp. 263-273. (doi:10.1198/jasa.2009.0125)

Xue, J. H. and Titterington, D. M. (2009) Interpretation of hybrid generative/discriminative algorithms. Neurocomputing, 72(7-9), pp. 1648-1655. (doi:10.1016/j.neucom.2008.08.009)

Wang, B. and Titterington, D.M. (2009) Variational Bayesian inference for partially observed stochastic dynamical systems. Journal of Physics: Conference Series, 143(1), 012-022. (doi:10.1088/1742-6596/143/1/012022)

2008

Xue, J.H. and Titterington, D.M. (2008) Comment on: "On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes". Neural Processing Letters, 28(3), pp. 169-187. (doi:10.1007/s11063-008-9088-7)

Xue, J.H. and Titterington, D.M. (2008) Do unbalanced data have a negative effect on LDA? Pattern Recognition, 41(5), pp. 1575-1588. (doi:10.1016/j.patcog.2007.11.008)

Xue, J.H. and Titterington, D.M. (2008) Short note on two output-dependent hidden Markov models. Pattern Recognition Letters, 29(9), pp. 1424-1426. (doi:10.1016/j.patrec.2008.02.018)

2007

Dolia, A., Harris, C., Shawe-Taylor, J. and Titterington, D. (2007) Kernel ellipsoidal trimming. Computational Statistics and Data Analysis, 52, pp. 309-324. (doi:10.1016/j.csda.2007.03.020)

McGrory, C. and Titterington, D. (2007) Variational approximations in Bayesian model selection for finite mixture distributions. Computational Statistics and Data Analysis, 51, pp. 5352-5367. (doi:10.1016/j.csda.2006.07.020)

Shi, J., Wang, B., Murray-Smith, R. and Titterington, D. (2007) Gaussian process functional regression Modeling for batch data. Biometrics, 63, pp. 714-723. (doi:10.1111/j.1541-0420.2007.00758.x)

Tanaka, K. and Titterington, D. (2007) Statistical trajectory of an approximate EM algorithm for probabilistic image processing. Journal of Physics A: Mathematical and Theoretical, 40, pp. 11285-11300. (doi:10.1088/1751-8113/40/37/007)

2006

Celeux, G., Forbes, F., Robert, C.P. and Titterington, D.M. (2006) Deviation information criteria for missing data models. Bayesian Analysis, 1(4), pp. 651-706. (doi:10.1214/06-BA122)

Dolia, A., De Bie, T., Harris, C., Shawe-Taylor, J. and Titterington, D. (2006) The minimum volume covering ellipsoid estimation in kernel-defined feature spaces. Machine Learning, 4212, pp. 630-637.

Titterington, M. (2006) Some aspects of latent structure analysis. Lecture Notes in Computer Science(3940), pp. 69-83. (doi:10.1007/11752790_4)

Titterington, M. (2006) Some aspects of statistical image modelling and restoration. In: Lyons, L. and Unel, M.K. (eds.) Statistical Problems in Particle Physics, Astrophysics and Cosmology: Proceedings of PHYSTAT05, Oxford, UK, 12-15 September 2005. Imperial College Press: London, UK, pp. 255-266. ISBN 9781860946493

2005

Tanaka, K. and Titterington, D. (2005) First-order phase transition and Bayesian image processing by loopy belief propagation. Progress of Theoretical Physics Supplement, pp. 288-291.

Wang, B. and Titterington, D. (2005) Variational Bayes estimation of mixing coefficients. Deterministic and Statistical Methods in Machine Learning, 3635, pp. 281-295.

2004

Tanaka, K., Shouno, H., Okada, M. and Titterington, D. (2004) Accuracy of the Bethe approximation for hyperparameter estimation in probabilistic image processing. Journal of Physics A: Mathematical and General, 37, pp. 8675-8695. (doi:10.1088/0305-4470/37/36/007)

Titterington, D. (2004) Bayesian methods for neural networks and related models. Statistical Science, 19, pp. 128-139. (doi:10.1214/088342304000000099)

Titterington, M. (2004) Statistical modeling and computation. In: Rubin, D.B., Gelman, A. and Meng, X.L. (eds.) Applied Bayesia Modeling and Causal Inference from Incomplete-Data Perspectives: an Essential Journey with Donald Rubin's Statistical Family. Series: Wiley series in probability and statistics. Wiley: New York, pp. 183-188. ISBN 9780470090435

Wang, B. and Titterington, D. (2004) Lack of consistency of mean field and variational Bayes approximations for state space models. Neural Processing Letters, 20, pp. 151-170.

Wang, B. and Titterington, M. (2004) Converegence and asymptotic normality of variational Bayesian approximations for exponential family models with missing values. In: Halperin, J.Y. and Chickering, M. (eds.) Uncertainty in Artificial Intelligence: Proceedings of the Twentieth Conference (2004), July 7-11, 2004, Banff, Canada. AUAI Press: Arlington, VA. USA, pp. 577-584. ISBN 9780974903903

2003

Fokoue, E. and Titterington, D. (2003) Mixtures of factor analysers. Bayesian estimation and inference by stochastic simulation. Machine Learning, 50, pp. 73-94.

Humphreys, K. and Titterington, D. (2003) Variational approximations for categorical causal modeling with latent variables. Psychometrika, 68, pp. 391-412.

Shi, J., Murray-Smith, R. and Titterington, D. (2003) Bayesian regression and classification using mixtures of Gaussian processes. International Journal of Adaptive Control and Signal Processing, 17, pp. 149-161. (doi:10.1002/acs.744)

Tanaka, K., Inoue, J. and Titterington, D.M. (2003) Probabilistic image processing by means of the Bethe approximation for the Q-Ising model. Journal of Physics A: Mathematical and General, 36, pp. 11023-11035. (doi:10.1088/0305-4470/36/43/025)

Tanaka, K., Inoue, J. and Titterington, M. (2003) Loopy belief propagation and probabilistic image processing. In: Adali, T., Larsen, J., van Hulle, M., Douglas, S. and Rouat, J. (eds.) 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (NNSP'03): Toulouse, France, 17-19 September, 2003. IEEE: Piscataway, N.J., USA, pp. 329-338. ISBN 9780780381773

2002

Casella, G., Mengersen, K.L., Robert, C.P. and Titterington, D.M. (2002) Perfect samplers for mixtures of distributions. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), pp. 777-790. (doi:10.1111/1467-9868.00360)

Hall, P., Humphreys, K. and Titterington, D.M. (2002) On the adequacy of variational lower bound functions for likelihood-based inference in Markovian models with missing values. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(3), pp. 549-564. (doi:10.1111/1467-9868.00350)

Archer, G. and Titterington, D. (2002) Parameter estimation for hidden Markov chains. Journal of Statistical Planning and Inference, 108, pp. 365-390.

2001

Humphreys, K. and Titterington, M. (2001) Some examples of recursive variational approximations. In: Opper, M. and Saad, D. (eds.) Advanced Mean Field Methods: Theory and Practice. Series: Neural information processing series. MIT Press: Cambridge, MA, pp. 179-195. ISBN 9780262150545

Stavropoulos, P. and Titterington, M. (2001) Improved particle filters and smoothing. In: de Freitas, N. and Gordon, N. (eds.) Sequential Monte Carlo Methods in Practice. Series: Statistics for engineering and information science. Springer: New York, pp. 295-317. ISBN 9780387951461

Titterington, M. (2001) Optimal design in flexible models, including feed-forward networks and nonparametric regression. In: Atkinson, A.C., Bogacka, B. and Zhigljavsky, A.A. (eds.) Optimum Design 2000. Series: Nonconvex optimization and its applications (51). Kluwer Academic: Dordrecht, The Netherlands, pp. 261-273. ISBN 9780792367987

1999

Kay, J.W. and Titterington, D.M. (Eds.) (1999) Statistics and Neural Networks: Advances at the Interface. Oxford University Press: Oxford. ISBN 9780198524229

1997

Craigmile, P.F. and Titterington, D.M. (1997) Parameter estimation for finite mixtures of uniform distributions. Communications in Statistics: Theory and Methods, 26(8), pp. 1981-1995. (doi:10.1080/03610929708832026)

1994

Gray, A.J., Kay, J.W. and Titterington, D.M. (1994) An empirical study of the simulation of various models used for images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(5), pp. 507-513. (doi:10.1109/34.291447)

1992

Gray, A. J., Kay, J.W. and Titterington, D.M. (1992) On the estimation of noisy binary Markov random fields. Pattern Recognition Letters, 25(7), pp. 749-768. (doi:10.1016/0031-3203(92)90138-9)

1991

Hall, P., Kay, J.W. and Titterington, D.M. (1991) On estimation of noise variance in two-dimensional signal processing. Advances in Applied Probability, 23(3), pp. 476-495.

Thomson, A.M., Kay, J.W. and Titterington, D.M. (1991) Noise estimation in signal restoration using regularisation. Biometrika, 78(3), pp. 475-488. (doi:10.1093/biomet/78.3.475)

Thomson, A.M., Brown, J.C., Kay, J.W. and Titterington, D.M. (1991) A study of methods of choosing the smoothing parameter in image restoration by regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(4), pp. 326-339. (doi:10.1109/34.88568)

1990

Hall, P., Kay, J.W. and Titterington, D.M. (1990) Asymptotically optimal difference-based estimation of variance in nonparametric regression. Biometrika, 77(3), pp. 521-528. (doi:10.1093/biomet/77.3.521)

1989

Thomson, A.M., Kay, J.W. and Titterington, D.M. (1989) A cautionary note about crossvalidatory choice. Journal of Statistical Computation and Simulation, 33(4), pp. 199-216. (doi:10.1080/00949658908811198)

1986

Kay, J.W. and Titterington, D.M. (1986) Image labelling and the statistical analysis of incomplete data. In: Second International Conference on Image Processing and its Applications: 24-26 June 1986 venue, Imperial College of Science and Technology, London. Series: IEE conference publication (265). Institution of Electrical Engineers: London. ISBN 9780852963333

1985

Ford, I. , Titterington, D. and Wu, C.F.J. (1985) Inference and sequential design. Biometrika, 72(3), pp. 545-551. (doi:10.2307/2336726)

This list was generated on Tue Nov 12 14:44:50 2019 GMT.
Number of items: 55.

Articles

Xue, J.H. and Titterington, D.M. (2011) Median-based image thresholding. Image and Vision Computing, 29(9), pp. 631-637. (doi:10.1016/j.imavis.2011.06.003)

Xue, J.H. and Titterington, D.M. (2011) The p-folded cumulative distribution function and the mean absolute deviation from the p-quantile. Statistics and Probability Letters, 81(8), pp. 1179-1182. (doi:10.1016/j.spl.2011.03.014)

Xue, J.H. and Titterington, D.M. (2011) t-tests, F-tests and Otsu's methods for image thresholding. IEEE Transactions on Image Processing, 20(8), pp. 2392-2396. (doi:10.1109/TIP.2011.2114358)

Cole, D.J., Morgan, B.J.T. and Titterington, D.M. (2010) Determining the parametric structure of models. Mathematical Biosciences, 228(1), pp. 16-30. (doi:10.1016/j.mbs.2010.08.004)

Xue, J. H. and Titterington, D. M. (2010) Joint discriminative-generative modelling based on statistical tests for classification. Pattern Recognition Letters, 31(9), pp. 1048-1055. (doi:10.1016/j.patrec.2010.01.015)

Xue, J. H. and Titterington, D. M. (2010) On the generative-discriminative tradeoff approach: Interpretation, asymptotic efficiency and classification performance. Computational Statistics and Data Analysis, 54(2), pp. 438-451. (doi:10.1016/j.csda.2009.09.011)

Hall, P., Titterington, D. M. and Xue, J. H. (2009) Median-Based Classifiers for High-Dimensional Data. Journal of the American Statistical Association, 104(488), pp. 1597-1608. (doi:10.1198/jasa.2009.tm08107)

Johnstone, I.M. and Titterington, D.M. (2009) Statistical challenges of high-dimensional data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367(1906), pp. 4237-4253. (doi:10.1098/rsta.2009.0159)

Hall, P., Titterington, D.M. and Xue, J. (2009) Tilting methods for assessing the influence of components in a classifier. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(4), pp. 783-803. (doi:10.1111/j.1467-9868.2009.00701.x)

McGrory, C.A., Titterington, D.M., Reeves, R. and Pettitt, A.N. (2009) Variational Bayes for estimating the parameters of a hidden Potts model. Statistics and Computing, 19(3), pp. 329-340. (doi:10.1007/s11222-008-9095-6)

McGrory, C. A. and Titterington, D. M. (2009) Variational Bayesian Analysis for Hidden Markov Models. Australian and New Zealand Journal of Statistics, 51(2), pp. 227-244. (doi:10.1111/j.1467-842X.2009.00543.x)

Cucala, L., Marin, J. M., Robert, C. P. and Titterington, D.M. (2009) A Bayesian Reassessment of Nearest-Neighbor Classification. Journal of the American Statistical Association, 104(485), pp. 263-273. (doi:10.1198/jasa.2009.0125)

Xue, J. H. and Titterington, D. M. (2009) Interpretation of hybrid generative/discriminative algorithms. Neurocomputing, 72(7-9), pp. 1648-1655. (doi:10.1016/j.neucom.2008.08.009)

Wang, B. and Titterington, D.M. (2009) Variational Bayesian inference for partially observed stochastic dynamical systems. Journal of Physics: Conference Series, 143(1), 012-022. (doi:10.1088/1742-6596/143/1/012022)

Xue, J.H. and Titterington, D.M. (2008) Comment on: "On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes". Neural Processing Letters, 28(3), pp. 169-187. (doi:10.1007/s11063-008-9088-7)

Xue, J.H. and Titterington, D.M. (2008) Do unbalanced data have a negative effect on LDA? Pattern Recognition, 41(5), pp. 1575-1588. (doi:10.1016/j.patcog.2007.11.008)

Xue, J.H. and Titterington, D.M. (2008) Short note on two output-dependent hidden Markov models. Pattern Recognition Letters, 29(9), pp. 1424-1426. (doi:10.1016/j.patrec.2008.02.018)

Dolia, A., Harris, C., Shawe-Taylor, J. and Titterington, D. (2007) Kernel ellipsoidal trimming. Computational Statistics and Data Analysis, 52, pp. 309-324. (doi:10.1016/j.csda.2007.03.020)

McGrory, C. and Titterington, D. (2007) Variational approximations in Bayesian model selection for finite mixture distributions. Computational Statistics and Data Analysis, 51, pp. 5352-5367. (doi:10.1016/j.csda.2006.07.020)

Shi, J., Wang, B., Murray-Smith, R. and Titterington, D. (2007) Gaussian process functional regression Modeling for batch data. Biometrics, 63, pp. 714-723. (doi:10.1111/j.1541-0420.2007.00758.x)

Tanaka, K. and Titterington, D. (2007) Statistical trajectory of an approximate EM algorithm for probabilistic image processing. Journal of Physics A: Mathematical and Theoretical, 40, pp. 11285-11300. (doi:10.1088/1751-8113/40/37/007)

Celeux, G., Forbes, F., Robert, C.P. and Titterington, D.M. (2006) Deviation information criteria for missing data models. Bayesian Analysis, 1(4), pp. 651-706. (doi:10.1214/06-BA122)

Dolia, A., De Bie, T., Harris, C., Shawe-Taylor, J. and Titterington, D. (2006) The minimum volume covering ellipsoid estimation in kernel-defined feature spaces. Machine Learning, 4212, pp. 630-637.

Titterington, M. (2006) Some aspects of latent structure analysis. Lecture Notes in Computer Science(3940), pp. 69-83. (doi:10.1007/11752790_4)

Tanaka, K. and Titterington, D. (2005) First-order phase transition and Bayesian image processing by loopy belief propagation. Progress of Theoretical Physics Supplement, pp. 288-291.

Wang, B. and Titterington, D. (2005) Variational Bayes estimation of mixing coefficients. Deterministic and Statistical Methods in Machine Learning, 3635, pp. 281-295.

Tanaka, K., Shouno, H., Okada, M. and Titterington, D. (2004) Accuracy of the Bethe approximation for hyperparameter estimation in probabilistic image processing. Journal of Physics A: Mathematical and General, 37, pp. 8675-8695. (doi:10.1088/0305-4470/37/36/007)

Titterington, D. (2004) Bayesian methods for neural networks and related models. Statistical Science, 19, pp. 128-139. (doi:10.1214/088342304000000099)

Wang, B. and Titterington, D. (2004) Lack of consistency of mean field and variational Bayes approximations for state space models. Neural Processing Letters, 20, pp. 151-170.

Fokoue, E. and Titterington, D. (2003) Mixtures of factor analysers. Bayesian estimation and inference by stochastic simulation. Machine Learning, 50, pp. 73-94.

Humphreys, K. and Titterington, D. (2003) Variational approximations for categorical causal modeling with latent variables. Psychometrika, 68, pp. 391-412.

Shi, J., Murray-Smith, R. and Titterington, D. (2003) Bayesian regression and classification using mixtures of Gaussian processes. International Journal of Adaptive Control and Signal Processing, 17, pp. 149-161. (doi:10.1002/acs.744)

Tanaka, K., Inoue, J. and Titterington, D.M. (2003) Probabilistic image processing by means of the Bethe approximation for the Q-Ising model. Journal of Physics A: Mathematical and General, 36, pp. 11023-11035. (doi:10.1088/0305-4470/36/43/025)

Casella, G., Mengersen, K.L., Robert, C.P. and Titterington, D.M. (2002) Perfect samplers for mixtures of distributions. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), pp. 777-790. (doi:10.1111/1467-9868.00360)

Hall, P., Humphreys, K. and Titterington, D.M. (2002) On the adequacy of variational lower bound functions for likelihood-based inference in Markovian models with missing values. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(3), pp. 549-564. (doi:10.1111/1467-9868.00350)

Archer, G. and Titterington, D. (2002) Parameter estimation for hidden Markov chains. Journal of Statistical Planning and Inference, 108, pp. 365-390.

Craigmile, P.F. and Titterington, D.M. (1997) Parameter estimation for finite mixtures of uniform distributions. Communications in Statistics: Theory and Methods, 26(8), pp. 1981-1995. (doi:10.1080/03610929708832026)

Gray, A.J., Kay, J.W. and Titterington, D.M. (1994) An empirical study of the simulation of various models used for images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(5), pp. 507-513. (doi:10.1109/34.291447)

Gray, A. J., Kay, J.W. and Titterington, D.M. (1992) On the estimation of noisy binary Markov random fields. Pattern Recognition Letters, 25(7), pp. 749-768. (doi:10.1016/0031-3203(92)90138-9)

Hall, P., Kay, J.W. and Titterington, D.M. (1991) On estimation of noise variance in two-dimensional signal processing. Advances in Applied Probability, 23(3), pp. 476-495.

Thomson, A.M., Kay, J.W. and Titterington, D.M. (1991) Noise estimation in signal restoration using regularisation. Biometrika, 78(3), pp. 475-488. (doi:10.1093/biomet/78.3.475)

Thomson, A.M., Brown, J.C., Kay, J.W. and Titterington, D.M. (1991) A study of methods of choosing the smoothing parameter in image restoration by regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(4), pp. 326-339. (doi:10.1109/34.88568)

Hall, P., Kay, J.W. and Titterington, D.M. (1990) Asymptotically optimal difference-based estimation of variance in nonparametric regression. Biometrika, 77(3), pp. 521-528. (doi:10.1093/biomet/77.3.521)

Thomson, A.M., Kay, J.W. and Titterington, D.M. (1989) A cautionary note about crossvalidatory choice. Journal of Statistical Computation and Simulation, 33(4), pp. 199-216. (doi:10.1080/00949658908811198)

Ford, I. , Titterington, D. and Wu, C.F.J. (1985) Inference and sequential design. Biometrika, 72(3), pp. 545-551. (doi:10.2307/2336726)

Book Sections

Titterington, M. (2006) Some aspects of statistical image modelling and restoration. In: Lyons, L. and Unel, M.K. (eds.) Statistical Problems in Particle Physics, Astrophysics and Cosmology: Proceedings of PHYSTAT05, Oxford, UK, 12-15 September 2005. Imperial College Press: London, UK, pp. 255-266. ISBN 9781860946493

Titterington, M. (2004) Statistical modeling and computation. In: Rubin, D.B., Gelman, A. and Meng, X.L. (eds.) Applied Bayesia Modeling and Causal Inference from Incomplete-Data Perspectives: an Essential Journey with Donald Rubin's Statistical Family. Series: Wiley series in probability and statistics. Wiley: New York, pp. 183-188. ISBN 9780470090435

Wang, B. and Titterington, M. (2004) Converegence and asymptotic normality of variational Bayesian approximations for exponential family models with missing values. In: Halperin, J.Y. and Chickering, M. (eds.) Uncertainty in Artificial Intelligence: Proceedings of the Twentieth Conference (2004), July 7-11, 2004, Banff, Canada. AUAI Press: Arlington, VA. USA, pp. 577-584. ISBN 9780974903903

Tanaka, K., Inoue, J. and Titterington, M. (2003) Loopy belief propagation and probabilistic image processing. In: Adali, T., Larsen, J., van Hulle, M., Douglas, S. and Rouat, J. (eds.) 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (NNSP'03): Toulouse, France, 17-19 September, 2003. IEEE: Piscataway, N.J., USA, pp. 329-338. ISBN 9780780381773

Humphreys, K. and Titterington, M. (2001) Some examples of recursive variational approximations. In: Opper, M. and Saad, D. (eds.) Advanced Mean Field Methods: Theory and Practice. Series: Neural information processing series. MIT Press: Cambridge, MA, pp. 179-195. ISBN 9780262150545

Stavropoulos, P. and Titterington, M. (2001) Improved particle filters and smoothing. In: de Freitas, N. and Gordon, N. (eds.) Sequential Monte Carlo Methods in Practice. Series: Statistics for engineering and information science. Springer: New York, pp. 295-317. ISBN 9780387951461

Titterington, M. (2001) Optimal design in flexible models, including feed-forward networks and nonparametric regression. In: Atkinson, A.C., Bogacka, B. and Zhigljavsky, A.A. (eds.) Optimum Design 2000. Series: Nonconvex optimization and its applications (51). Kluwer Academic: Dordrecht, The Netherlands, pp. 261-273. ISBN 9780792367987

Kay, J.W. and Titterington, D.M. (1986) Image labelling and the statistical analysis of incomplete data. In: Second International Conference on Image Processing and its Applications: 24-26 June 1986 venue, Imperial College of Science and Technology, London. Series: IEE conference publication (265). Institution of Electrical Engineers: London. ISBN 9780852963333

Edited Books

Kay, J.W. and Titterington, D.M. (Eds.) (1999) Statistics and Neural Networks: Advances at the Interface. Oxford University Press: Oxford. ISBN 9780198524229

Conference Proceedings

Tanaka, K., Morin, N., Yasuda, M. and Titterington, D.M. (2009) Probabilistic Image Processing by Extended Gauss-Markov Random Fields. In: IEEE/SP 15th Workshop on Statistical Signal Processing, 2009, Cardiff, August 31st to September 3rd, 2009, pp. 618-621. (doi:10.1109/SSP.2009.5278499)

This list was generated on Tue Nov 12 14:44:50 2019 GMT.