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David J. C. MacKay
Researcher at University of Cambridge
Publications - 95
Citations - 38789
David J. C. MacKay is an academic researcher from University of Cambridge. The author has contributed to research in topics: Bayesian probability & Artificial neural network. The author has an hindex of 50, co-authored 95 publications receiving 36341 citations. Previous affiliations of David J. C. MacKay include University of California, San Francisco & California Institute of Technology.
Papers
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Book
Information Theory, Inference and Learning Algorithms
TL;DR: A fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
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Bayesian interpolation
TL;DR: The Bayesian approach to regularization and model-comparison is demonstrated by studying the inference problem of interpolating noisy data by examining the posterior probability distribution of regularizing constants and noise levels.
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Good error-correcting codes based on very sparse matrices
TL;DR: It is proved that sequences of codes exist which, when optimally decoded, achieve information rates up to the Shannon limit, and experimental results for binary-symmetric channels and Gaussian channels demonstrate that practical performance substantially better than that of standard convolutional and concatenated codes can be achieved.
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Near Shannon limit performance of low density parity check codes
TL;DR: The authors report the empirical performance of Gallager's low density parity check codes on Gaussian channels, showing that performance substantially better than that of standard convolutional and concatenated codes can be achieved.
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A practical Bayesian framework for backpropagation networks
TL;DR: A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks that automatically embodies "Occam's razor," penalizing overflexible and overcomplex models.