Radford Neal

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Ian H. WittenProfessor of Computer Science, University of Waikato, New ZealandEmail verificata su waikato.ac.nz
Geoffrey HintonEmeritus Prof. Computer Science, University of TorontoEmail verificata su cs.toronto.edu
David MacKayProfessor of Natural Philosophy, Cavendish Laboratory, University of CambridgeEmail verificata su mrao.cam.ac.uk
Richard ZemelProfessor of Computer Science, University of TorontoEmail verificata su cs.toronto.edu
Sam Roweis (Memorial)New York UniversityEmail verificata su buffalo.edu
W.R. PeltierProfessor of Physics, University of TorontoEmail verificata su atmosp.physics.utoronto.ca
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Bradley P. CarlinPharmaLex - USEmail verificata su counterpointstat.com
Andrew GelmanProfessor of Statistics and Political Science, Columbia UniversityEmail verificata su stat.columbia.edu
Alistair MoffatSchool of Computing and Information Systems, The University of MelbourneEmail verificata su unimelb.edu.au
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Brian WyvillProfessor of Computer Science, University of Victoria, BCEmail verificata su cs.uvic.ca
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Radford Neal

U Toronto *** GOOGLE SCHOLAR CAN GIVE WRONG PUBLICATION DATE/REFERENCE - LOOK AT ACTUAL PAPER/BOOK!

Email verificata su utstat.toronto.edu - Home page

Bayesian inference Monte Carlo methods Information theory Machine learning Neural networks


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Bayesian learning for neural networks RM Neal Springer Science & Business Media, 1995	6936*	1995
Near Shannon limit performance of low density parity check codes DJC MacKay, RM Neal Electronics letters 33 (6), 457-458, 1997	5249	1997
Arithmetic coding for data compression IH Witten, RM Neal, JG Cleary Communications of the ACM 30 (6), 520-540, 1987	4602	1987
MCMC Using Hamiltonian Dynamics R Neal Handbook of Markov Chain Monte Carlo, 113-162, 2011	4480	2011
A view of the EM algorithm that justifies incremental, sparse, and other variants RM Neal, GE Hinton Learning in graphical models, 355-368, 1998	3709	1998
Markov chain sampling methods for Dirichlet process mixture models RM Neal Journal of computational and graphical statistics 9 (2), 249-265, 2000	3269	2000
Slice sampling RM Neal Annals of statistics, 705-741, 2003	2913	2003
Probabilistic inference using Markov chain Monte Carlo methods RM Neal Department of Computer Science, University of Toronto, 1993	2464	1993
Annealed importance sampling RM Neal Statistics and computing 11, 125-139, 2001	1834	2001
The helmholtz machine P Dayan, GE Hinton, RM Neal, RS Zemel Neural computation 7 (5), 889-904, 1995	1753	1995
The" wake-sleep" algorithm for unsupervised neural networks GE Hinton, P Dayan, BJ Frey, RM Neal Science 268 (5214), 1158-1161, 1995	1580	1995
Markov chain Monte Carlo in practice: a roundtable discussion RE Kass, BP Carlin, A Gelman, RM Neal The American Statistician 52 (2), 93-100, 1998	918	1998
Arithmetic coding revisited A Moffat, RM Neal, IH Witten ACM Transactions on Information Systems (TOIS) 16 (3), 256-294, 1998	865	1998
Connectionist learning of belief networks RM Neal Artificial intelligence 56 (1), 71-113, 1992	773	1992
Good codes based on very sparse matrices DJC MacKay, RM Neal IMA International Conference on Cryptography and Coding, 100-111, 1995	719	1995
Regression and classification using Gaussian process priors RM Neal Bayesian Statistics 6, 1998	690*	1998
Monte Carlo implementation of Gaussian process models for Bayesian regression and classification RM Neal arXiv preprint physics/9701026, 1997	630	1997
A split-merge Markov chain Monte Carlo procedure for the Dirichlet process mixture model S Jain, RM Neal Journal of Computational and Graphical Statistics, 2004	625	2004
Priors for infinite networks RM Neal	501	1994
Sampling from multimodal distributions using tempered transitions RM Neal Statistics and computing 6, 353-366, 1996	474	1996

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