David J. C. MacKay

Iain Murray, Ryan Prescott Adams, David J. C. MacKay: Elliptical slice sampling. Journal of Machine Learning Research - Proceedings Track 9: 541-548 (2010)

Keith Vertanen, David J. C. MacKay: Speech dasher: fast writing using speech and gaze. CHI 2010: 595-598

Oliver Stegle, Linda Payet, Jean-Louis Mergny, David J. C. MacKay, Julian Leon Huppert: Predicting and understanding the stability of G-quadruplexes. Bioinformatics 25(12) (2009)

Ryan Prescott Adams, Iain Murray, David J. C. MacKay: Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. ICML 2009: 2

Oliver Stegle, Sebastian V. Fallert, David J. C. MacKay, Søren Brage: Gaussian Process Robust Regression for Noisy Heart Rate Data. IEEE Trans. Biomed. Engineering 55(9): 2143-2151 (2008)

Ryan Prescott Adams, Iain Murray, David J. C. MacKay: The Gaussian Process Density Sampler. NIPS 2008: 9-16

Iain Murray, Zoubin Ghahramani, David J. C. MacKay: MCMC for Doubly-intractable Distributions. UAI 2006

Iain Murray, David J. C. MacKay, Zoubin Ghahramani, John Skilling: Nested sampling for Potts models. NIPS 2005

David J. C. MacKay, G. Mitchison, P. L. McFadden: Sparse-graph codes for quantum error correction. IEEE Transactions on Information Theory 50(10): 2315-2330 (2004)

Dan Witzner Hansen, David J. C. MacKay, John Paulin Hansen, Mads Nielsen: Eye tracking off the shelf. ETRA 2004: 58

David H. Stern, Thore Graepel, David J. C. MacKay: Modelling Uncertainty in the Game of Go. NIPS 2004

David J. C. MacKay: Information theory, inference, and learning algorithms. Cambridge University Press 2003, isbn 978-0-521-64298-9, pp. I-XII, 1-628

Ann-Marie Martoglio, James W. Miskin, Stephen K. Smith, David J. C. MacKay: A decomposition model to track gene expression signatures: preview on observer-independent classification of ovarian cancer. Bioinformatics 18(12): 1617-1624 (2002)

David J. C. MacKay, Christopher P. Hesketh: Performance of low density parity check codes as a function of actual and assumed noise levels. Electr. Notes Theor. Comput. Sci. 74: 89-96 (2002)

David J. C. MacKay, Michael S. Postol: Weaknesses of Margulis and Ramanujan-Margulis low-density parity-check cCodes. Electr. Notes Theor. Comput. Sci. 74: 97-104 (2002)

David J. Ward, David J. C. MacKay: Fast Hands-free Writing by Gaze Direction. CoRR cs.HC/0204030 (2002)

Virginia R. de Sa, David J. C. MacKay: Model fitting as an aid to bridge balancing in neuronal recording. Neurocomputing 38-40: 1651-1656 (2001)

Matthew C. Davey, David J. C. MacKay: Reliable communication over channels with insertions, deletions, and substitutions. IEEE Transactions on Information Theory 47(2): 687-698 (2001)

David J. C. MacKay: Errata for "Good error-correcting codes based on very sparse matrices". IEEE Transactions on Information Theory 47(5): 2101 (2001)

David J. C. MacKay: Almost-Certainly Runlength-Limiting Codes. IMA Int. Conf. 2001: 138-147

Mark N. Gibbs, David J. C. MacKay: Variational Gaussian process classifiers. IEEE Trans. Neural Netw. Learning Syst. 11(6): 1458-1464 (2000)

David J. Ward, Alan F. Blackwell, David J. C. MacKay: Dasher - a data entry interface using continuous gestures and language models. UIST 2000: 129-137

David J. C. MacKay: Comparison of Approximate Methods for Handling Hyperparameters. Neural Computation 11(5): 1035-1068 (1999)

David J. C. MacKay, Simon T. Wilson, Matthew C. Davey: Comparison of constructions of irregular Gallager codes. IEEE Transactions on Communications 47(10): 1449-1454 (1999)

David J. C. MacKay: Good Error-Correcting Codes Based on Very Sparse Matrices. IEEE Transactions on Information Theory 45(2): 399-431 (1999)

Oliver B. Downs, David J. C. MacKay, Daniel D. Lee: The Nonnegative Boltzmann Machine. NIPS 1999: 428-434

Matthew C. Davey, David J. C. MacKay: Low-density parity check codes over GF(q). IEEE Communications Letters 2(6): 165-167 (1998)

Robert J. McEliece, David J. C. MacKay, Jung-Fu Cheng: Turbo Decoding as an Instance of Pearl's "Belief Propagation" Algorithm. IEEE Journal on Selected Areas in Communications 16(2): 140-152 (1998)

David J. C. MacKay: Choice of Basis for Laplace Approximation. Machine Learning 33(1): 77-86 (1998)

Brendan J. Frey, David J. C. MacKay: A Revolution: Belief Propagation in Graphs with Cycles. NIPS 1997

David J. C. MacKay: Good Codes Based on Very Sparse Matrices. IMA Conf. 1995: 100-111

Kenneth D. Miller, David J. C. MacKay: The Role of Constraints in Hebbian Learning. Neural Computation 6(1): 100-126 (1994)

David J. C. MacKay: A Free Energy Minimization Framework for Inference Problems in modulo 2 Arithmetic. FSE 1994: 179-195

David J. C. MacKay: Bayesian Interpolation. Neural Computation 4(3): 415-447 (1992)

David J. C. MacKay: A Practical Bayesian Framework for Backpropagation Networks. Neural Computation 4(3): 448-472 (1992)

David J. C. MacKay: Information-Based Objective Functions for Active Data Selection. Neural Computation 4(4): 590-604 (1992)

David J. C. MacKay: The Evidence Framework Applied to Classification Networks. Neural Computation 4(5): 720-736 (1992)

David J. C. MacKay: Bayesian Model Comparison and Backprop Nets. NIPS 1991: 839-846

John S. Bridle, Anthony J. R. Heading, David J. C. MacKay: Unsupervised Classifiers, Mutual Information and 'Phantom Targets'. NIPS 1991: 1096-1101

David J. C. MacKay, Kenneth D. Miller: Analysis of Linsker's Simulations of Hebbian Rules. NIPS 1989: 694-701