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Bayesian Learning for Neural Networks

• Geoffrey E. Hinton, Radford M. Neal
• 1995

A view of the EM algorithm that justifies incremental, sparse, and other variants

• Radford M. Neal, Geoffrey E. Hinton
• 1999

Probabilistic Inference Using Markov Chain Monte Carlo Methods

• Radford M. Neal
• 1993

Probabilistic inference is an attractive approach to uncertain reasoning and em pirical learning in arti cial intelligence Computational di culties arise however because probabilistic models with the… (More)

MCMC using Hamiltonian dynamics

• Radford M. Neal
• 2010

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… (More)

Arithmetic Coding for Data Compression

• Ian H. Witten, Radford M. Neal, John G. Cleary
• Commun. ACM
• 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… (More)

Annealed importance sampling

• Radford M. Neal
• Statistics and Computing
• 2001

Abstract. Simulated annealing — moving from a tractable distribution to a distribution of interest via a sequence of intermediate distributions — has traditionally been used as an inexact method of… (More)

Bayesian leaning for neural networks

• Radford M. Neal
• 1996

Connectionist Learning of Belief Networks

• Radford M. Neal
• Artif. Intell.
• 1992

Neal, R.M., Connectionist learning of belief networks, Artificial Intelligence 56 (1992) 71-113. Connectionist learning procedures are presented for "sigmoid" and "noisy-OR" varieties of… (More)

Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification

• Radford M. Neal
• 1997

Gaussian processes are a natural way of defining prior distributions over functions of one or more input variables. In a simple nonparametric regression problem, where such a function gives the mean… (More)

A Split-merge Markov Chain Monte Carlo Procedure for the Dirichlet Process Mixture Model

• Radford M. Neal
• 2000

We propose a split-merge Markov chain algorithm to address the problem of inee-cient sampling for conjugate Dirichlet process mixture models. Traditional Markov chain Monte Carlo methods for Bayesian… (More)

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