D. Titterington

发表

On the use of Gibbs Markov chain models in the analysis of images based on second-order pairwise interactive distributions

W. Qian, D. Titterington, 1989 .

Recursive Parameter Estimation Using Incomplete Data

D. Titterington, 1984 .

Comparison of Discrimination Techniques Applied to a Complex Data Set of Head Injured Patients

D. Titterington, G. D. Murray, J. Habbema, 1981 .

Estimation Problems with Data from a Mixture

D. Titterington, G. D. Murray, 1978 .

Recent advances in nonlinear experiment design

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

Parameter estimation for hidden Markov chains

D. Titterington, G. Archer, 2002 .

Convergence properties of a general algorithm for calculating variational Bayesian estimates for a normal mixture model

D. Titterington, Bo Wang, 2006 .

Minimum Distance Non-Parametric Estimation of Mixture Proportions

D. Titterington, 1983 .

Neural Networks and Related Methods for Classification - Discussion

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

Tilting methods for assessing the influence of components in a classifier

P. Hall, D. Titterington, Jing-Hao Xue, 2009 .

Sure independence screening for ultrahigh dimensional feature space Discussion

Jeffrey S. Morris, Gareth M. James, Hao Helen Zhang, 2008 .

Common Structure of Techniques for Choosing Smoothing Parameters in Regression Problems

P. Hall, D. Titterington, 1987 .

A cautionary note about crossvalidatory choice

D. Titterington, J. Kay, A. Thompson, 1989 .

Minimum Covering Ellipses

D. Titterington, B. Silverman, Bernard Walter Silverman, 1980 .

Estimation of Correlation Coefficients by Ellipsoidal Trimming

D. Titterington, 1978 .

Convergence and Asymptotic Normality of Variational Bayesian Approximations for Expon

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

On the adequacy of variational lower bound functions for likelihood‐based inference in Markovian models with missing values

P. Hall, D. Titterington, D. M. Titterington, 2002 .

Variational Bayesian Inference for Partially Observed Diusions

D. Titterington, Bo Wang, 2003 .

Approximate Bayesian inference for simple mixtures

D. Titterington, K. Humphreys, 2000 .

Statistical challenges of high-dimensional data

I. Johnstone, D. Titterington, 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

On some Bayesian/regularization methods for image restoration

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

Asymptotically optimal difference-based estimation of variance in nonparametric regression

P. Hall, D. Titterington, J. Kay, 1990 .

Noise estimation in signal restoration using regularization

D. Titterington, A. M. Thompson, J. Kay, 1991 .

On estimation of noise variance in two-dimensional signal processing

P. Hall, D. Titterington, J. Kay, 1991, Advances in Applied Probability.

The Use of Uncategorized Data to Improve the Performance of a Nonparametric Estimator of a Mixture Density

P. Hall, D. Titterington, 1985 .

Neural Networks: A Review from a Statistical Perspective

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

Some recent research in the analysis of mixture distributions

D. Titterington, 1990 .

Improved Particle Filters and Smoothing

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

Bayesian inference in hidden Markov modelsthrough reversible jump Markov chain Monte

D. Titterington, CarloChristian, P. Robert, 2022 .

Inadequacy of interval estimates corresponding to variational Bayesian approximations

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

Local convergence of variational Bayes estimators for mixing coefficients

D. Titterington, Bo Wang, 2003 .

Parameter estimation for hidden Gibbs chains

W. Qian, D. Titterington, 1990 .

Stochastic relaxations and em algorithms for markov random fields

W. Qian, D. Titterington, 1992 .

Updating a Diagnostic System using Unconfirmed Cases

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

Optimal design for curve estimation by local linear smoothing

P. Hall, D. Titterington, Ming-Yen Cheng, 1998 .

Two-sample test statistics for measuring discrepancies between two multivariate probability density functions using kernel-based density estimates

N. H. Anderson, P. Hall, D. Titterington, 1994 .

Kernel‐Based Density Estimates from Incomplete Data

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

The EM Algorithm, Variational Approximations and Expectation Propagation for Mixtures

D. Michael Titterington, D. Titterington, Kerrie L Mengersen, 2011 .

Variational approximations for categorical causal modeling with latent variables

D. Titterington, K. Humphreys, 2003 .

Parameter estimation in latent profile models

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

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

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

Edge-preserving and peak-preserving smoothing

P. Hall, D. Titterington, 1992 .

Another look at a Bayesian direct deconvolution method

D. Titterington, C. Rossi, 1985 .

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

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

An Image Analysis Problem in Electron Microscopy

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

The Effect of Simulation Order on Level Accuracy and Power of Monte Carlo Tests

P. Hall, D. Titterington, 1989 .

Introduction to Gelfand and Smith (1990) Sampling-Based Approaches to Calculating Marginal Densities

D. Titterington, 1997 .

Estimation of parameters in hidden Markov models

W. Qian, D. Titterington, 1991, Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences.

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

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

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

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

Multidimensional Markov Chain Models for Image Textures

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

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

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

Cross-validation in nonparametric estimation of probabilities and probability densities

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

Median-based image thresholding

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

American Society for Quality Edge-Preserving and Peak-Preserving Smoothing

P. Hall, D. Titterington, 2012 .

Some examples of recursive variational approximations for Bayesian inference

Kevin Humphreys, Dm Titterington, D. Titterington, 2001 .

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.

A comparison of two statistics for detecting clustering in one dimension

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

Boltzmann machines: Statistical associations and algorithms for training

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

Discussion on the paper by Friedman and Fisher

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

Analysis of latent structure models with multidimensional latent variables

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

Comment on “Estimating Parameters in Continuous Univariate Distributions”

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

Recent Developments in Statistical Inference and Data Analysis

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

Image labelling and the statistical analysis of incomplete data

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

Learning with large data sets using filtered {G}aussian Process priors

Roderick Murray-Smith, Barak A. Pearlmutter, Jian Qing Shi, 2005 .

The importance of good blood pressure control in the prevention of stroke recurrence in hypertensive patients

D. Titterington, F. Dunn, D. Beevers, 1981, Postgraduate medical journal.

DISCUSSION ON THE MEETING ON THE GIBBS SAMPLER AND OTHER MARKOV CHAIN-MONTE CARLO METHODS

J Besag, Christopher Jennison, Dm Titterington, 1993 .

Variational Bayesian inference for partially observed stochastic dynamical systems

D. Titterington, Bo Wang, 2009 .

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

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

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

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

Some Aspects of Statistical Image Modelling and Restoration

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

Hierarchical Gaussian process mixtures for regression

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

Transdimensional sequential Monte Carlo using variational Bayes - SMCVB

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

Computational Bayesian Analysis of Hidden Markov Models

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

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 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.

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

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

Advances in the understanding of retinitis pigmentosa

D. Titterington, G. Dutton, K. Moos, 1992, Eye.

Midfacial fractures and the eye: the development of a system for detecting patients at risk of eye injury.

D. Titterington, K. Moos, A. el-Attar, 1991, The British journal of oral & maxillofacial surgery.

Midfacial fractures and the eye: the development of a system for detecting patients at risk of eye injury--a prospective evaluation.

D. Titterington, K. Moos, A. el-Attar, 1991, The British journal of oral & maxillofacial surgery.

Relation between prognosis and the blood pressure before and during treatment of hypertensive patients.

D. Titterington, F. Dunn, D. Beevers, 1978, Clinical science and molecular medicine. Supplement.

On the fitting of parallel isochrons and the method of maximum likelihood

D. Titterington, A. Halliday, D. Titterington, 1979 .

Median-Based Classifiers for High-Dimensional Data

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

Calculation of 230ThU isochrons, ages, and errors

D. Titterington, K. Ludwig, 1994 .

Do unbalanced data have a negative effect on LDA?

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

Kernel ellipsoidal trimming

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

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..

On the estimation of noisy binary Markov random fields

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

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

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

A small selection of neural network methods and their statistical connections

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

Threshold Selection from Image Histograms with Skewed Components Based on Maximum-likelihood Estimation of Skew-normal and Log-concave Distributions

Jing-Hao Xue, D. Michael Titterington, D. Titterington, 2011 .

Discriminative Image Thresholding

Jing-Hao Xue, D. Michael Titterington, D. Titterington, 2007 .

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.

Bayesian Image Modeling by Means of a Generalized Sparse Prior and Loopy Belief Propagation

Kazuyuki Tanaka, Muneki Yasuda, D. Michael Titterington, 2012 .

Statistical trajectory of an approximate EM algorithm for probabilistic image processing

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

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

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

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).

On Smoothing Sparse Multinomial Data

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

Determining the parametric structure of models.

D M Titterington, B J T Morgan, D J Cole, 2010, Mathematical biosciences.

Efficient Nonparametric Estimation of Mixture Proportions

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

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.

The exploration of new methods for learning in binary Boltzmann machines

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

On partial local smoothing rules for curve estimation

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

The maximum entropy method for data analysis

D. Titterington, D. Titterington, 1984, Nature.

On the shrinkage of local linear curve estimators

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

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.

Variational Bayes for estimating the parameters of a hidden Potts model

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

Statistics and Neural Networks

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

Gaussian Process Functional Regression Modeling for Batch Data

J Q Shi, B Wang, R Murray-Smith, 2007, Biometrics.

Ridge Finding from Noisy Data

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

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

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

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.

Curve estimation when the design density is low

Michael H. Neumann, J. Marron, P. Hall, 1997 .

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

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

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..

Variational Bayes Estimation of Mixing Coefficients

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

Variational approximations in Bayesian model selection for finite mixture distributions

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

Imputation of missing values using density estimation

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

A comparative study of smoothing procedures for ordered categorical data

D. Titterington, A. Bowman, 1985 .

Interpolation rules suggested by asymptotic expansions

N. H. Anderson, P. Hall, D. Titterington, 1992 .