D. M. Titterington

发表

D. R. Cox, D. M. Titterington, 1991, Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences.

Image labelling and the statistical analysis of incomplete data

D. M. Titterington, Jim Kay, D. Titterington, 1986 .

Comments on "Application of the Conditional Population-Mixture Model to Image Segmentation"

D. M. Titterington, D. Titterington, 1984, IEEE Trans. Pattern Anal. Mach. Intell..

The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces

John Shawe-Taylor, Tijl De Bie, D. M. Titterington, 2006, ECML.

Aspects of Optimal Design in Dynamic Systems

D. M. Titterington, D. Titterington, 1980 .

Variational Bayes for estimating the parameters of a hidden Potts model

Anthony N. Pettitt, D. M. Titterington, Clare A. McGrory, 2009, Stat. Comput..

Improved Particle Filters and Smoothing

D. M. Titterington, Photis Stavropoulos, D. Titterington, 2001, Sequential Monte Carlo Methods in Practice.

On perfect simulation for some mixtures of distributions

Christian P. Robert, D. M. Titterington, James P. Hobert, 1999, Stat. Comput..

Boltzmann machines: Statistical associations and algorithms for training

D. M. Titterington, N. H. Anderson, D. Titterington, 1998 .

Beyond the binary Boltzmann machine

D. M. Titterington, Niall H. Anderson, 1995, IEEE Trans. Neural Networks.

Loopy belief propagation and probabilistic image processing

Kazuyuki Tanaka, D. M. Titterington, J. Inoue, 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

Comment on “On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes”

D. M. Titterington, Jing-Hao Xue, D. Titterington, 2008, Neural Processing Letters.

Bayesian Image Restoration: An Application to Edge-Preserving Surface Recovery

D. M. Titterington, Wei Qian, W. Qian, 1993, IEEE Trans. Pattern Anal. Mach. Intell..

Recent advances in nonlinear experiment design

D. M. Titterington, Christos P. Kitsos, Ian Ford, 1989 .

Updating a Diagnostic System using Unconfirmed Cases

D. M. Titterington, D. Titterington, 1976 .

Analysis of latent structure models with multidimensional latent variables

D. M. Titterington, A. P. Dunmur, D. Titterington, 2000 .

On the estimation of noisy binary Markov random fields

D. M. Titterington, Jim Kay, Alison J. Gray, 1992, Pattern Recognit..

Computational Bayesian Analysis of Hidden

D. M. Titterington, 2016 .

Improving the Mean-Field Approximation in Belief Networks Using Bahadur's Reparameterisation of the Multivariate Binary Distribution

D. M. Titterington, Keith Humphreys, D. Titterington, 2000, Neural Processing Letters.

Inadequacy of interval estimates corresponding to variational Bayesian approximations

Bo Wang, D. M. Titterington, D. Titterington, 2005, AISTATS.

Variational Bayes Estimation of Mixing Coefficients

Bo Wang, D. M. Titterington, D. Titterington, 2004, Deterministic and Statistical Methods in Machine Learning.

Hierarchical Gaussian process mixtures for regression

Roderick Murray-Smith, D. M. Titterington, Jian Qing Shi, 2005, Stat. Comput..

Statistics and Neural Networks

D. M. Titterington, Jim Kay, D. Titterington, 2000, Technometrics.

Some Methods for Investigating Spatial Clustering, with Epidemiological Applications

D. M. Titterington, N. H. Anderson, 1997 .

Some Aspects of Latent Structure Analysis

D. M. Titterington, 2005, SLSFS.

Complex stochastic systems

D. M. Titterington, Andrew B. Lawson, David R. Cox, 1994 .

Probabilistic image processing based on the Q-Ising model by means of the mean-field method and loopy belief propagation

Kazuyuki Tanaka, D. M. Titterington, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

Reparameterization strategies for hidden Markov models and Bayesian approaches to maximum likelihood estimation

Christian P. Robert, D. M. Titterington, C. Robert, 1998, Stat. Comput..

A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization

D. M. Titterington, Jim Kay, John C. Brown, 1991, IEEE Trans. Pattern Anal. Mach. Intell..

Discussion on the paper by Friedman and Fisher

D. M. Titterington, D. Titterington, 1999, Stat. Comput..

Interpretation of hybrid generative/discriminative algorithms

D. M. Titterington, Jing-Hao Xue, 2009, Neurocomputing.

Lack of Consistency of Mean Field and Variational Bayes Approximations for State Space Models

Bo Wang, D. M. Titterington, D. Titterington, 2004, Neural Processing Letters.

Optimal Design in Flexible Models, Including Feed-Forward Networks and Nonparametric Regression

D. M. Titterington, D. Titterington, 2001 .

On the shrinkage of local linear curve estimators

Peter Hall, D. M. Titterington, Ming-Yen Cheng, 1997, Stat. Comput..

Synthesis of magnetic material images using SAR models with irregular neighbour sets

D. M. Titterington, C. Shang, 2000 .

Kernel ellipsoidal trimming

John Shawe-Taylor, D. M. Titterington, Christopher J. Harris, 2007, Comput. Stat. Data Anal..

Computational Bayesian Analysis of Hidden Markov Mesh Models

D. M. Titterington, A. P. Dunmur, 1997, IEEE Trans. Pattern Anal. Mach. Intell..

Neural Networks: A Review from a Statistical Perspective

D. M. Titterington, Bing Cheng, D. Titterington, 1994 .

Modelling magnetic material images with simultaneous autoregressions

D. M. Titterington, Changjing Shang, 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

Convergence and Asymptotic Normality of Variational Bayesian Approximations for Expon

Bo Wang, D. M. Titterington, D. Titterington, 2004, UAI.

Mixtures: Estimation and Applications

Kerrie Mengersen, Christian P. Robert, D. M. Titterington, 2011 .

Filtered Gaussian Processes for Learning with Large Data-Sets

Barak A. Pearlmutter, D. M. Titterington, Roderick Murray-Smith, 2003, European Summer School on Multi-AgentControl.

Mixtures of Factor Analysers. Bayesian Estimation and Inference by Stochastic Simulation

D. M. Titterington, Ernest Fokoué, D. Titterington, 2004, Machine Learning.

The exploration of new methods for learning in binary Boltzmann machines

D. M. Titterington, Keith Humphreys, D. Titterington, 1999, AISTATS.

An alternative stochastic supervisor in discriminant analysis

D. M. Titterington, 1989, Pattern Recognit..

Some Aspects of Statistical Image Modelling and Restoration

D. M. Titterington, D. Titterington, 2006 .

Mean fields and two-dimensional Markov random fields in image analysis

D. M. Titterington, A. P. Dunmur, D. Titterington, 1998, Pattern Analysis and Applications.

t -Tests, F -Tests and Otsu's Methods for Image Thresholding

D. M. Titterington, Jing-Hao Xue, D. Titterington, 2011, IEEE Trans. Image Process..

Pixel labelling for three-dimensional scenes based on Markov mesh models

D. M. Titterington, Wei Qian, W. Qian, 1991, Signal Process..

Do unbalanced data have a negative effect on LDA?

D. M. Titterington, Jing-Hao Xue, D. Titterington, 2008, Pattern Recognit..

Bayesian Methods for Neural Networks and Related Models

D. M. Titterington, D. Titterington, 2004 .

Statistics and Neural Networks

D. M. Titterington, Jim Kay, 2000, Technometrics.

A small selection of neural network methods and their statistical connections

D. M. Titterington, Bing Cheng, D. Titterington, 1994 .

On some Bayesian/regularization methods for image restoration

D. M. Titterington, G. Archer, D. Titterington, 1995, IEEE Trans. Image Process..

On a Modification to the Mean Field EM Algorithm in Factorial Learning

D. M. Titterington, A. P. Dunmur, 1996, NIPS.

Variational approximations in Bayesian model selection for finite mixture distributions

D. M. Titterington, Clare A. McGrory, D. Titterington, 2007, Comput. Stat. Data Anal..

[Neural Networks: A Review from Statistical Perspective]: Rejoinder

D. M. Titterington, Bing Cheng, D. Titterington, 1994 .

Transdimensional sequential Monte Carlo using variational Bayes - SMCVB

Anthony N. Pettitt, D. M. Titterington, Clare A. McGrory, 2016, Comput. Stat. Data Anal..

Choosing the regularization parameter in image restoration

D. M. Titterington, D. Titterington, 1991 .

Joint discriminative-generative modelling based on statistical tests for classification

D. M. Titterington, Jing-Hao Xue, 2010, Pattern Recognit. Lett..

On the generative-discriminative tradeoff approach: Interpretation, asymptotic efficiency and classification performance

D. M. Titterington, Jing-Hao Xue, D. Titterington, 2010, Comput. Stat. Data Anal..

Median-based image thresholding

D. M. Titterington, Jing-Hao Xue, D. Titterington, 2011, Image Vis. Comput..

Neural Networks and Related Methods for Classification - Discussion

Trevor Hastie, Robert Tibshirani, David J. Hand, 1994 .

A Re-Examination of the Distance-Weighted k-Nearest Neighbor Classification Rule

Andrew Luk, D. M. Titterington, James E. S. Macleod, 1987, IEEE Transactions on Systems, Man, and Cybernetics.

An Empirical Study of the Simulation of Various Models used for Images

D. M. Titterington, Jim Kay, Alison J. Gray, 1994, IEEE Trans. Pattern Anal. Mach. Intell..

Short note on two output-dependent hidden Markov models

D. M. Titterington, Jing-Hao Xue, 2008, Pattern Recognit. Lett..

Statistical analysis of the expectation-maximization algorithm with loopy belief propagation in Bayesian image modeling

D. M. Titterington, Shun Kataoka, Muneki Yasuda, 2012 .

The Quality and Relevance of Peripheral Neuropathy Data on a Diabetic Clinical Information System

David Kerr, D. M. Titterington, D. Titterington, 1992, Diabetic medicine : a journal of the British Diabetic Association.

Ridge Finding from Noisy Data

D. M. Titterington, Peter Hall, P. Hall, 1992 .

Recent Developments in Statistical Inference and Data Analysis

D. M. Titterington, D. Titterington, 1982 .

The influence of initial conditions on maximum likelihood estimation of the parameters of a binary hidden Markov model

D. M. Titterington, A. P Dunmur, 1998 .

A note on the distribution of the likelihood ratio statistic for normal mixture models with known proportions

D. M. Titterington, A. Polymenis, 1999 .

A comparison of two statistics for detecting clustering in one dimension

D. M. Titterington, N. H. Anderson, D. Titterington, 1995 .

On some smoothing techniques used in image restoration

D. M. Titterington, Peter Hall, 1986 .

First-Order Phase Transition and Bayesian Image Processing by Loopy Belief Propagation

D. M. Titterington, Kazuyuki Tanaka, 2005 .

Kernel‐Based Density Estimates from Incomplete Data

D. M. Titterington, D. Titterington, G. M. Mill, 1983 .

Computational Bayesian Analysis of Hidden Markov Models

D. M. Titterington, Tobias Rydén, D. Titterington, 1998 .

Fractal model of a one-dimensional discrete signal and its implementation

D. M. Titterington, X. Zhu, B. Cheng, 1994 .

Statistical trajectory of an approximate EM algorithm for probabilistic image processing

D. M. Titterington, Kazuyuki Tanaka, D. Titterington, 2007 .

On confidence bands in nonparametric density estimation and regression

D. M. Titterington, Peter Hall, 1988 .

Bayesian regression and classification using mixtures of Gaussian processes

D. M. Titterington, Roderick Murray-Smith, Jian Qing Shi, 2003 .

An Image Analysis Problem in Electron Microscopy

D. M. Titterington, J. N. Chapman, W. Qian, 1996 .

Comment on “Estimating Parameters in Continuous Univariate Distributions”

D. M. Titterington, D. Titterington, 1985 .

Cross-correlation between simultaneously generated sequences of pseudo-random uniform deviates

D. M. Titterington, N. H. Anderson, 1993 .

On partial local smoothing rules for curve estimation

D. M. Titterington, J. Stephen Marron, Peter Hall, 1995 .

Imputation of missing values using density estimation

D. M. Titterington, Joseph Sedransk, D. Titterington, 1989 .

A Comparative Study of Kernel-Based Density Estimates for Categorical Data

D. M. Titterington, D. Titterington, 1980 .

On Smoothing Sparse Multinomial Data

D. M. Titterington, Peter Hall, P. Hall, 1987 .

Probabilistic image processing by means of the Bethe approximation for the Q-Ising model

D. M. Titterington, Jun-ichi Inoue, Kazuyuki Tanaka, 2003 .

Efficient Nonparametric Estimation of Mixture Proportions

D. M. Titterington, Peter Hall, P. Hall, 1984 .

Accuracy of the Bethe approximation for hyperparameter estimation in probabilistic image processing

Masato Okada, D. M. Titterington, Hayaru Shouno, 2004 .

Cross-validation in nonparametric estimation of probabilities and probability densities

D. M. Titterington, Adrian Bowman, Peter Hall, 1984 .

Multidimensional Markov Chain Models for Image Textures

D. M. Titterington, W. Qian, D. Titterington, 1991 .

On the deter-mination of the number of components in a mixture

D. M. Titterington, D. Titterington, A. Polymenis, 1998 .

Parameter estimation in latent profile models

D. M. Titterington, A. P. Dunmur, D. Titterington, 1998 .

Median-Based Classifiers for High-Dimensional Data

D. M. Titterington, Jing-Hao Xue, Peter Hall, 2009 .