Papers by topic:

Brain Imaging
Probability and Stats
Bayesian Learning
Gaussian Mixture Models
Autoregressive Models
Independent Component Analysis
Bayesian neural nets
Hidden Markov Models
Other Nonstationary Models
Error bars
Brain Computer Interfacing
Biomedical Applications
Neural Networks
Digital Neural Networks

Brain Imaging
  • W.D. Penny, J. Ashburner, S. Kiebel, R. Henson, D.E. Glaser, C. Phillips and K. Friston (2001) Statistical Parametric Mapping: An Annotated Bibliography. Wellcome Department of Cognitive Neurology, 2001 PDF , Postscript , HTML or Text

  • W.D. Penny and K. Friston (2002) Spatio-Temporal Clustering HBM: Eighth International Conference on Functional Mapping of the Human Brain, June 2-6, 2002, Sendai, Japan Text file , Poster ,

  • W.D. Penny, S. Kiebel and K. Friston (2002) Variational Bayesian Inference for fMRI time series HBM: Eighth International Conference on Functional Mapping of the Human Brain, June 2-6, 2002, Sendai, Japan Text file , Poster ,

    Probability and Statistics
  • W.D. Penny (2001) Notes on Random Effects Analysis Wellcome Department of Cognitive Neurology, 2001 Postscript .

  • W.D. Penny (2001) The Normal, Chi^2, t and F Probability Distributions. Wellcome Department of Cognitive Neurology, 2001 Postscript

  • W.D. Penny (2001) Kullback-Liebler Divergences of Normal, Gamma, Dirichlet and Wishart Densities. Wellcome Department of Cognitive Neurology, 2001 Postscript ,

    Bayesian Learning
  • M.R. Johnson, C.D. Good, W.D. Penny, P.R.J. Barnes and J.W. Scadding (2001) Playing the odds in clinical decision making: lessons from berry aneuryisms undetected by magnetic resonance angiography British Medical Journal 322, 2 June 2001, pp. 1347-1349.

  • W.D. Penny and S.J. Roberts (2000) Notes on Variational Learning Technical Report PARG-00-1, Department of Engineering Science, Oxford University.

    Gaussian Mixture Models
  • W.D. Penny (2001) Variational Bayes for d-dimensional Gaussian mixture models Wellcome Department of Cognitive Neurology, University College London.

  • W.D. Penny and S.J. Roberts (2000) Variational Bayes for 1-dimensional mixture models Technical Report PARG-00-2, Department of Engineering Science, Oxford University. An earlier version of this paper contained an incorrect expression for the KL divergence between two Dirichlet densities. This version contains the correct expression.

  • Stephen J. Roberts, Dirk Husmeier, Iead Rezek & Will Penny (1998). Bayesian Approaches to Gaussian Mixture Modelling. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 20, No. 11, pp. 1133-1142.

    Bayesian Autoregressive Models
  • W.D. Penny and S.J. Roberts (2000) Variational Bayes for Generalised Autoregressive Models Technical Report PARG-00-12, Department of Engineering Science, Oxford University.

  • W.D. Penny and S.J. Roberts (2000) Bayesian Multivariate Autoregressive Models with Structured Priors Technical Report PARG-00-11, Department of Engineering Science, Oxford University.

  • W.D. Penny and S.J. Roberts (2000) Bayesian Methods for Autoregressive Models IEEE Workshop on Neural Networks for Signal Processing, Sydney Australia, December 2000.

  • W.D. Penny and S.J. Roberts (2000) Variational Bayes for Non-Gaussian Autoregressive Models IEEE Workshop on Neural Networks for Signal Processing, Sydney Australia, December 2000. The paper that appears in the actual conference proceedings contains an incorrect expression for the KL divergence between two Dirichlet densities. This version contains the correct expression.

    Independent Component Analysis
  • S.J. Roberts and W.D. Penny (2001) Mixtures of Independent Component Analysers To appear in Proceedings of ICANN 2001, Vienna.

  • R. Choudrey, W.D. Penny and S.J. Roberts (2000) An Ensemble Learning Approach to Independent Component Analysis IEEE Workshop on Neural Networks for Signal Processing, Sydney Australia, December 2000.

  • W.D. Penny, S.J. Roberts and R. Everson (2000) ICA: Model-order selection and dynamic source models In S.J. Roberts and R. Everson (Eds.) ICA: Principles and Practice, Cambridge University Press, pp. 299-314.

  • W.D. Penny, R. Everson and S.J. Roberts (1999) Hidden Markov Independent Component Analysis In M Girolami (Ed.) Advances in Independent Component Analysis, Springer (2000).

  • W.D. Penny, S.J. Roberts and R. Everson (2000) Hidden Markov Independent Components for Biosignal Analysis Proceedings of MEDSIP-2000, International Conference on Advances in Medical Signal and Information Processing, IEE, pp. 244-250.

    Bayesian Neural Networks
  • W.D. Penny, D. Husmeier and S.J. Roberts (1999) The Bayesian Paradigm: second generation neural computing In P. Lisboa (Ed.) Artificial Neural Networks in Biomedicine , Springer-Verlag.

  • D. Husmeier, W. D. Penny and S. J. Roberts (1999). An Empirical Evaluation of Bayesian Sampling with Hybrid Monte Carlo for Training Neural Network Classifiers. Neural Networks Vol 12, pp. 677-705.

  • Dirk Husmeier, William D. Penny, Stephen J. Roberts (1998). Empirical Evaluation of Bayesian Sampling for Neural Classifiers. in: L.Niklason, M.Boden, T.Ziemke (Eds.), ICANN 98: Proceedings of the 8th International Conference on Artificial Neural Networks , Springer Verlag, Perspectives in Neural Computing, pp. 323-328.

  • W.D. Penny and S.J. Roberts (1998). Bayesian neural networks for classification: how useful is the evidence framework ? Neural Networks , Vol 12, pp. 877-892.

    Hidden Markov Models
  • W.D. Penny and S.J. Roberts (1998) Hidden Markov Models with Extended Observation Densities Technical Report TR-98-15, Department of Electrical Engineering, Imperial College.

  • W.D. Penny and S.J. Roberts (1999) Dynamic models for nonstationary signal segmentation . Computers and Biomedical Research. Vol 32, No.6, December,pp.483-502. A longer technical report version is available here.

  • W.D. Penny and S.J. Roberts (1998). Gaussian Observation Hidden Markov Models for EEG analysis. Technical Report TR-98-12, Department of Electrical Engineering, Imperial College.

    Other Nonstationary Models
  • W.D. Penny, S.J. Roberts (1999) Dynamic Logistic Regression In Proceedings IJCNN'99 .

  • W.D. Penny, S.J. Roberts (1999) Nonstationary Logistic Regression Technical Report, Department of Electrical Engineering, Imperial College.

  • W.D. Penny and S.J. Roberts (1998). Dynamic Linear Models, Recursive Least Squares and Steepest Descent Learning. Technical Report TR-98-13, Department of Electrical Engineering, Imperial College.

    Error bars

  • W.D. Penny, D. Husmeier and S.J. Roberts (1999). Covariance-based weighting for optimal combination of model predictions ICANN-99 , Vol 2, pp. 826-831. Longer, earlier technical report here .

  • W.D. Penny and S.J. Roberts (1998). Error bars for linear and nonlinear neural network regression models. Technical Report, Department of Electrical Engineering, Imperial College.

  • S.J. Roberts and W.D. Penny (1996). A Maximum Certainty Approach to Feedforward Neural Networks. Electronic Letters , 29(15),1340-1341.

  • S.J. Roberts, W.D. Penny, D. Pillot (1996) Novelty, Confidence & Errors in Connectionist Systems. Proceedings of IEE Colloquium on Intelligent Sensors and Fault Detection , September 1996, 1996/261 : 10/1-10/6.

    Brain Computer Interfacing
  • S.J. Roberts and W.D. Penny (2000) Real-time Brain Computer Interfacing: a preliminary study using Bayesian learning Medical and Biological Engineering and Computing , Vol 38, No. 1, pp.56-61, 2000.

  • W.D. Penny, S.J. Roberts, E. Curran and M. Stokes(2000) EEG-based communication: a pattern recognition approach IEEE Transactions on Rehabilitation Engineering, Vol 8, No. 2, June.

  • W.D. Penny, S.J. Roberts and M. Stokes(1999) EEG-based communication: a pattern recognition approach Brain-Computer Interface Technology: Theory and practice. First International Meeting, Rensselaerville, New York, June 1999.

  • W.D. Penny and S.J. Roberts (1999) Experiments with an EEG-based computer interface Technical Report, Department of Electrical Engineering, Imperial College.

  • W.D. Penny, S.J. Roberts (1999) EEG-based communication via dynamic neural network models In Proceedings IJCNN'99 .

  • W.D. Penny, S.J. Roberts and M.J. Stokes (1998). Imagined Hand Movements Identified from the EEG Mu-Rhythm. Technical Report. Department of Electrical Engineering, Imperial College .

  • S.J. Roberts, W. Penny & I.Rezek (1998). Temporal and Spatial Complexity measures for EEG-based Brain-Computer Interfacing. Medical & Biological Engineering and Computing, Vol 37, No. 1, pp. 93-99.

  • W.D. Penny and S.J. Roberts (1997). Bayesian neural networks for detection of imagined finger movements from single-trial EEG. Technical Report, Department of Electrical Engineering, Imperial College.

    Other Biomedical Applications

  • W.D.Penny and D.Frost (1997). Neural network modelling of the level of observation decision in an acute psychiatric ward. Computers and Biomedical Research , 30, 1-17.

  • W.D.Penny and D.Frost (1996). Modelling psychiatric decisions with linear regression and neural networks. In J.Taylor (Ed.) Neural networks and their applications , John Wiley.

    Neural Network Tutorials

  • S.J. Roberts & W.D. Penny (1996). Neural Networks : Friends or Foes? Sensor Review . 17(1),64-70.

  • W.D.Penny and D.Frost (1996). Neural networks in clinical medicine. Medical Decision Making , 16(4),386-398.

    Digital Neural Networks

  • W.D.Penny and T.J.Stonham (1995). Generalization in multilayer sigma-pi networks. IEEE transactions on Neural Networks , 6(2), 506-508.

  • W.D.Penny and T.J.Stonham (1993) Storage capacity of multilayer boolean neural networks. Electronics Letters , 29(15), 1340-1341.