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Radford Neal
Radford Neal
U Toronto *** GOOGLE SCHOLAR CAN GIVE WRONG PUBLICATION DATE/REFERENCE - LOOK AT ACTUAL PAPER/BOOK!
Verified email at utstat.toronto.edu - Homepage
Title
Cited by
Cited by
Year
Bayesian learning for neural networks
RM Neal
Springer Science & Business Media, 1995
6624*1995
Near Shannon limit performance of low density parity check codes
DJC MacKay, RM Neal
Electronics letters 33 (6), 457-458, 1997
51591997
Arithmetic coding for data compression
IH Witten, RM Neal, JG Cleary
Communications of the ACM 30 (6), 520-540, 1987
45321987
MCMC Using Hamiltonian Dynamics
R Neal
Handbook of Markov Chain Monte Carlo, 113-162, 2011
41282011
A view of the EM algorithm that justifies incremental, sparse, and other variants
RM Neal, GE Hinton
Learning in graphical models, 355-368, 1998
36771998
Markov chain sampling methods for Dirichlet process mixture models
RM Neal
Journal of computational and graphical statistics 9 (2), 249-265, 2000
32252000
Slice sampling
RM Neal
Annals of statistics, 705-741, 2003
28222003
Probabilistic inference using Markov chain Monte Carlo methods
RM Neal
Department of Computer Science, University of Toronto, 1993
23801993
Annealed importance sampling
RM Neal
Statistics and computing 11, 125-139, 2001
17482001
The helmholtz machine
P Dayan, GE Hinton, RM Neal, RS Zemel
Neural computation 7 (5), 889-904, 1995
16941995
The" wake-sleep" algorithm for unsupervised neural networks
GE Hinton, P Dayan, BJ Frey, RM Neal
Science 268 (5214), 1158-1161, 1995
15361995
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
8951998
Arithmetic coding revisited
A Moffat, RM Neal, IH Witten
ACM Transactions on Information Systems (TOIS) 16 (3), 256-294, 1998
8591998
Connectionist learning of belief networks
RM Neal
Artificial intelligence 56 (1), 71-113, 1992
7581992
Good codes based on very sparse matrices
DJC MacKay, RM Neal
IMA International Conference on Cryptography and Coding, 100-111, 1995
7131995
Regression and classification using Gaussian process priors
RM Neal
Bayesian Statistics 6, 1998
677*1998
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
6182004
Monte Carlo implementation of Gaussian process models for Bayesian regression and classification
RM Neal
arXiv preprint physics/9701026, 1997
6081997
Sampling from multimodal distributions using tempered transitions
RM Neal
Statistics and computing 6, 353-366, 1996
4591996
Priors for infinite networks
RM Neal
4531994
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