Radford Neal
Radford Neal
Emeritus Professor, Dept. of Statistics and Dept. of Computer Science, University of Toronto
Verified email at utstat.toronto.edu - Homepage
TitleCited byYear
Near Shannon limit performance of low density parity check codes
DJC MacKay, RM Neal
Electronics letters 33 (6), 457-458, 1997
41831997
Bayesian learning for neural networks
RM Neal
Springer Science & Business Media, 1996
3832*1996
Arithmetic coding for data compression
IH Witten, RM Neal, JG Cleary
Communications of the ACM 30 (6), 520-540, 1987
37331987
A view of the EM algorithm that justifies incremental, sparse, and other variants
RM Neal, GE Hinton
Learning in graphical models, 355-368, 1998
27321998
Markov chain sampling methods for Dirichlet process mixture models
RM Neal
Journal of computational and graphical statistics 9 (2), 249-265, 2000
23652000
Probabilistic inference using Markov chain Monte Carlo methods
RM Neal
Department of Computer Science, University of Toronto, 1993
18301993
Slice sampling
RM Neal
Annals of statistics, 705-741, 2003
17942003
MCMC Using Hamiltonian Dynamics
R Neal
Handbook of Markov Chain Monte Carlo, 113-162, 2011
14502011
The helmholtz machine
P Dayan, GE Hinton, RM Neal, RS Zemel
Neural computation 7 (5), 889-904, 1995
10521995
Annealed importance sampling
RM Neal
Statistics and computing 11 (2), 125-139, 2001
10502001
The" wake-sleep" algorithm for unsupervised neural networks
GE Hinton, P Dayan, BJ Frey, RM Neal
Science 268 (5214), 1158-1161, 1995
9641995
Arithmetic coding revisited
A Moffat, RM Neal, IH Witten
ACM Transactions on Information Systems (TOIS) 16 (3), 256-294, 1998
7431998
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
5891998
Good codes based on very sparse matrices
DJC MacKay, RM Neal
IMA International Conference on Cryptography and Coding, 100-111, 1995
5801995
Connectionist learning of belief networks
RM Neal
Artificial intelligence 56 (1), 71-113, 1992
5431992
Monte Carlo implementation of Gaussian process models for Bayesian regression and classification
RM Neal
arXiv preprint physics/9701026, 1997
5231997
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
4522004
Regression and classification using Gaussian process priors
RM Neal
Bayesian Statistics 6, 1998
438*1998
Sampling from multimodal distributions using tempered transitions
RM Neal
Statistics and computing 6 (4), 353-366, 1996
3391996
Bayesian mixture modeling
RM Neal
Maximum Entropy and Bayesian Methods, 197-211, 1992
2131992
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