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Pattern Recognition and Machine Learning
- Radford M. Neal
- Mathematics, ArtTechnometrics
- 1 August 2007
TLDR
MCMC Using Hamiltonian Dynamics
- Radford M. Neal
- Physics
- 10 May 2011
Hamiltonian dynamics can be used to produce distant proposals for the Metropolis algorithm, thereby avoiding the slow exploration of the state space that results from the diffusive behaviour of…
Bayesian Learning for Neural Networks
- Radford M. Neal
- Computer Science
- 1995
TLDR
Markov Chain Sampling Methods for Dirichlet Process Mixture Models
- Radford M. Neal
- Mathematics
- 1 June 2000
Abstract This article reviews Markov chain methods for sampling from the posterior distribution of a Dirichlet process mixture model and presents two new classes of methods. One new approach is to…
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
- Radford M. Neal, Geoffrey E. Hinton
- Mathematics, Computer ScienceLearning in Graphical Models
- 26 March 1998
TLDR
Annealed importance sampling
- Radford M. Neal
- MathematicsStat. Comput.
- 8 March 1998
TLDR
Probabilistic Inference Using Markov Chain Monte Carlo Methods
- Radford M. Neal
- Computer Science
- 2011
TLDR
Arithmetic coding for data compression
- I. Witten, Radford M. Neal, J. Cleary
- Computer ScienceCACM
- 1 June 1987
The state of the art in data compression is arithmetic coding, not the better-known Huffman method. Arithmetic coding gives greater compression, is faster for adaptive models, and clearly separates…
Near Shannon limit performance of low density parity check codes
- D. Mackay, Radford M. Neal
- Computer Science
- 29 August 1996
The authors report the empirical performance of Gallager's low density parity check codes on Gaussian channels. They show that performance substantially better than that of standard convolutional and…
Slice Sampling
- Radford M. Neal
- MathematicsThe Annals of Statistics
- 2003
Markov chain sampling methods that adapt to characteristics of the distribution being sampled can be constructed using the principle that one can ample from a distribution by sampling uniformly from…
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