W. Penny. Simple Associative Learning. Technical Report, UEA School of Psychology, 2018. PDF , Matlab Code
W. Penny. Statistical Mechanics. Technical Report, Wellcome Trust Centre for Neuroimaging, 2015. PDF
W. Penny. Optimal Combination of Contrasts. Technical Report, Wellcome Trust Centre for Neuroimaging, 2015. PDF
W. Penny. Bayesian Inference for the Multivariate Normal. Technical Report, Wellcome Trust Centre for Neuroimaging, 2014. PDF
S. Bengtsson and W. Penny. A Bayesian model of the rule association task and effect of priming. Technical Report, Wellcome Trust Centre for Neuroimaging, 2013. PDF
W. Penny. Bayesian Parameter Averaging for GLMs. Technical Report, Wellcome Trust Centre for Neuroimaging, 2013. PDF
W. Penny. Dimensionality Tests for Canonical Variates Analysis. Technical Report, Wellcome Trust Centre for Neuroimaging, 2013. PDF
W. Penny. Bayesian General Linear Models with T-Priors. Technical Report, (Original UCL Version 2013) this version, School of Psychology, University of East Anglia, 2020. PDF
W.D. Penny and G. Flandin. Bayesian analysis of single-subject fMRI: SPM implementation. Technical report, Wellcome Department of Imaging Neuroscience, 2005.
K.E. Stephan, K.J. Friston, and W.D. Penny. Computing the objective function in DCM. Technical report, Wellcome Department of Imaging Neuroscience, ION, UCL, 2005.
R.N.A. Henson and W.D. Penny. ANOVAs and SPM. Technical report, Wellcome Department of Imaging Neuroscience, 2003. PDF
W.D. Penny. Wavelet smoothing of fMRI activation images. Technical report, Wellcome Department of Imaging Neuroscience, ION, UCL, 2002. PDF
W.D. Penny. Kullback-Liebler Divergences of Normal, Gamma, Dirichlet and Wishart Densities. Technical report, Wellcome Department of Cognitive Neurology, 2001.
W.D. Penny. Statistical Parametric Mapping: an annotated bibliography. Technical report, Wellcome Department of Imaging Neuroscience, ION, UCL, 2001.
W.D. Penny. The Normal, Chi^2, t and F Probability Distributions. Technical report, Wellcome Department of Cognitive Neurology, 2001.
W.D. Penny. Variational Bayes for d-dimensional Gaussian mixture models. Technical report, Wellcome Department of Cognitive Neurology, University College London, 2001.
W.D. Penny and S.J. Roberts. Notes on Variational Learning. Technical report, Department of Engineering Science, Oxford University, 2001.
W.D. Penny and S.J. Roberts. Variational Bayes for 1-dimensional mixture models. Technical report, Department of Engineering Science, Oxford University, 2000.
W.D. Penny and S.J. Roberts. Dynamic Linear Models, Recursive Least Squares and Steepest Descent Learning. Technical report, Department of Electrical Engineering, Imperial College, London, 1998.
W.D. Penny and S.J. Roberts. Error bars for linear and nonlinear neural network regression models. Technical report, Department of Electrical Engineering, Imperial College, London, 1998.
W.D. Penny and S.J. Roberts. Gaussian Observation Hidden Markov Models for EEG analysis. Technical report, Department of Electrical Engineering, Imperial College, London, 1998.
W.D. Penny and S.J. Roberts. Hidden Markov Models with Extended Observation Densities. Technical report, Department of Electrical Engineering, Imperial College, London, 1998.
W.D. Penny, S.J. Roberts, and M.J. Stokes. Imagined hand movements identified from the EEG mu-rhythm. Technical report, Department of Electrical Engineering, Imperial College, London, 1998.
W.D. Penny and S.J. Roberts. Bayesian neural networks for detection of imagined finger movements from single-trial EEG. Technical report, Department of Electrical Engineering, Imperial College, London, 1997.