Radford M. Neal

According to our database, Radford M. Neal authored at least 31 papers between 1983 and 2020.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2020

Non-reversibly updating a uniform [0, 1] value for Metropolis accept/reject decisions.

Radford M. Neal

CoRR, 2020

2015

Fast exact summation using small and large superaccumulators.

Radford M. Neal

CoRR, 2015

Representing numeric data in 32 bits while preserving 64-bit precision.

Radford M. Neal

CoRR, 2015

2014

Split Hamiltonian Monte Carlo.

Babak Shahbaba, Shiwei Lan, Wesley O. Johnson, Radford M. Neal

Stat. Comput., 2014

2012

Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals

Chunyi Wang, Radford M. Neal

CoRR, 2012

2009

Nonlinear Models Using Dirichlet Process Mixtures.

Babak Shahbaba, Radford M. Neal

J. Mach. Learn. Res., 2009

2007

Pattern Recognition and Machine Learning.

Radford M. Neal

Technometrics, 2007

Difference detection in LC-MS data for protein biomarker discovery.

Jennifer Listgarten, Radford M. Neal, Sam T. Roweis, Peter Wong, Andrew Emili

Bioinform., 2007

2006

Gene function classification using Bayesian models with hierarchy-based priors.

Babak Shahbaba, Radford M. Neal

BMC Bioinform., 2006

Modeling Dyadic Data with Binary Latent Factors.

Edward Meeds, Zoubin Ghahramani, Radford M. Neal, Sam T. Roweis

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure.

Jennifer Listgarten, Radford M. Neal, Sam T. Roweis, Rachel Puckrin, Sean Cutler

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees.

Radford M. Neal, Jianguo Zhang

Proceedings of the Feature Extraction - Foundations and Applications, 2006

2005

Classification with Bayesian Neural Networks.

Radford M. Neal

Proceedings of the Machine Learning Challenges, 2005

2004

Multiple Alignment of Continuous Time Series.

Jennifer Listgarten, Radford M. Neal, Sam T. Roweis, Andrew Emili

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

2003

Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models.

Radford M. Neal, Matthew J. Beal, Sam T. Roweis

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

2001

Annealed importance sampling.

Radford M. Neal

Stat. Comput., 2001

2000

On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note).

Radford M. Neal

J. Artif. Intell. Res., 2000

Inference for Belief Networks Using Coupling From the Past.

Michael Harvey, Radford M. Neal

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

1998

Arithmetic Coding Revisited.

Alistair Moffat, Radford M. Neal, Ian H. Witten

ACM Trans. Inf. Syst., 1998

A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants.

Radford M. Neal, Geoffrey E. Hinton

Proceedings of the Learning in Graphical Models, 1998

Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation.

Radford M. Neal

Proceedings of the Learning in Graphical Models, 1998

1997

Factor Analysis Using Delta-Rule Wake-Sleep Learning.

Radford M. Neal, Peter Dayan

Neural Comput., 1997

1996

Sampling from multimodal distributions using tempered transitions.

Radford M. Neal

Stat. Comput., 1996

1995

Bayesian learning for neural networks.

Radford M. Neal

PhD thesis, 1995

The Helmholtz machine.

Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel

Neural Comput., 1995

1993

Comments on 'A theoretical analysis of Monte Carlo algorithms for the simulation of Gibbs random field images'.

Radford M. Neal

IEEE Trans. Inf. Theory, 1993

1992

Asymmetric Parallel Boltzmann Machines are Belief Networks.

Radford M. Neal

Neural Comput., 1992

Connectionist Learning of Belief Networks.

Radford M. Neal

Artif. Intell., 1992

Bayesian Learning via Stochastic Dynamics.

Radford M. Neal

Proceedings of the Advances in Neural Information Processing Systems 5, [NIPS Conference, Denver, Colorado, USA, November 30, 1992

1987

Arithmetic Coding for Data Compression.

Ian H. Witten, Radford M. Neal, John G. Cleary

Commun. ACM, 1987

1983

Jade: A Distributed Software Prototyping Environment.

Ian H. Witten, Graham M. Birtwistle, John G. Cleary, David R. Hill, Danny Levinson, Greg Lomow, Radford M. Neal, Murray Peterson, Brian W. Unger, Brian Wyvill

ACM SIGOPS Oper. Syst. Rev., 1983