David J. C. MacKay

	2009
33		Ryan Prescott Adams, Iain Murray, David J. C. MacKay: Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. ICML 2009: 2
32		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)
	2008
31		Ryan Prescott Adams, Iain Murray, David J. C. MacKay: The Gaussian Process Density Sampler. NIPS 2008: 9-16
	2006
30		Iain Murray, Zoubin Ghahramani, David J. C. MacKay: MCMC for Doubly-intractable Distributions. UAI 2006
	2005
29		Iain Murray, David J. C. MacKay, Zoubin Ghahramani, John Skilling: Nested sampling for Potts models. NIPS 2005
	2004
28		Dan Witzner Hansen, David J. C. MacKay, John Paulin Hansen, Mads Nielsen: Eye tracking off the shelf. ETRA 2004: 58
27		David H. Stern, Thore Graepel, David J. C. MacKay: Modelling Uncertainty in the Game of Go. NIPS 2004
26		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)
	2002
25		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)
24		David J. Ward, David J. C. MacKay: Fast Hands-free Writing by Gaze Direction CoRR cs.HC/0204030: (2002)
23		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: (2002)
22		David J. C. MacKay, Michael S. Postol: Weaknesses of Margulis and Ramanujan-Margulis low-density parity-check cCodes. Electr. Notes Theor. Comput. Sci. 74: (2002)
	2001
21		David J. C. MacKay: Almost-Certainly Runlength-Limiting Codes. IMA Int. Conf. 2001: 138-147
20		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)
19		David J. C. MacKay: Errata for "Good error-correcting codes based on very sparse matrices". IEEE Transactions on Information Theory 47(5): 2101 (2001)
18		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)
	2000
17		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
	1999
16		Oliver B. Downs, David J. C. MacKay, Daniel D. Lee: The Nonnegative Boltzmann Machine. NIPS 1999: 428-434
15		David J. C. MacKay: Good Error-Correcting Codes Based on Very Sparse Matrices. IEEE Transactions on Information Theory 45(2): 399-431 (1999)
14		David J. C. MacKay: Comparison of Approximate Methods for Handling Hyperparameters. Neural Computation 11(5): 1035-1068 (1999)
	1998
13		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)
12		David J. C. MacKay: Choice of Basis for Laplace Approximation. Machine Learning 33(1): 77-86 (1998)
	1997
11		Brendan J. Frey, David J. C. MacKay: A Revolution: Belief Propagation in Graphs with Cycles. NIPS 1997
	1995
10		David J. C. MacKay: Good Codes Based on Very Sparse Matrices. IMA Conf. 1995: 100-111
	1994
9		David J. C. MacKay: A Free Energy Minimization Framework for Inference Problems in modulo 2 Arithmetic. FSE 1994: 179-195
8		Kenneth D. Miller, David J. C. MacKay: The Role of Constraints in Hebbian Learning. Neural Computation 6(1): 100-126 (1994)
	1992
7		David J. C. MacKay: Bayesian Interpolation. Neural Computation 4(3): 415-447 (1992)
6		David J. C. MacKay: A Practical Bayesian Framework for Backpropagation Networks. Neural Computation 4(3): 448-472 (1992)
5		David J. C. MacKay: Information-Based Objective Functions for Active Data Selection. Neural Computation 4(4): 590-604 (1992)
4		David J. C. MacKay: The Evidence Framework Applied to Classification Networks. Neural Computation 4(5): 720-736 (1992)
	1991
3		John S. Bridle, Anthony J. R. Heading, David J. C. MacKay: Unsupervised Classifiers, Mutual Information and 'Phantom Targets'. NIPS 1991: 1096-1101
2		David J. C. MacKay: Bayesian Model Comparison and Backprop Nets. NIPS 1991: 839-846
	1989
1		David J. C. MacKay, Kenneth D. Miller: Analysis of Linsker's Simulations of Hebbian Rules. NIPS 1989: 694-701

Coauthor Index

1	Ryan Prescott Adams	[31] [33]
2	Alan F. Blackwell	[17]
3	John S. Bridle	[3]
4	Jung-Fu Cheng	[13]
5	Matthew C. Davey	[20]
6	Oliver B. Downs	[16]
7	Brendan J. Frey	[11]
8	Zoubin Ghahramani	[29] [30]
9	Thore Graepel	[27]
10	Dan Witzner Hansen	[28]
11	John Paulin Hansen	[28]
12	Anthony J. R. Heading	[3]
13	Christopher P. Hesketh	[23]
14	Julian Leon Huppert	[32]
15	Daniel D. Lee	[16]
16	Ann-Marie Martoglio	[25]
17	Robert J. McEliece	[13]
18	P. L. McFadden	[26]
19	Jean-Louis Mergny	[32]
20	Kenneth D. Miller	[1] [8]
21	James W. Miskin	[25]
22	G. Mitchison	[26]
23	Iain Murray	[29] [30] [31] [33]
24	Mads Nielsen	[28]
25	Linda Payet	[32]
26	Michael S. Postol	[22]
27	Virginia R. de Sa	[18]
28	John Skilling	[29]
29	Stephen K. Smith	[25]
30	Oliver Stegle	[32]
31	David H. Stern	[27]
32	David J. Ward	[17] [24]

Colors in the list of coauthors