Efficiency of reversible MCMC methods: elementary derivations and applications to composite methods - Radford M. Neal; Jeffrey S. Rosenthal (J. Appl. Probab., 2025)

Non-reversibly updating a uniform [0, 1] value for Metropolis accept/reject decisions - Radford M. Neal (CoRR, 2020)

Representing numeric data in 32 bits while preserving 64-bit precision - Radford M. Neal (CoRR, 2015)

Fast exact summation using small and large superaccumulators - Radford M. Neal (CoRR, 2015)

Split Hamiltonian Monte Carlo - Babak Shahbaba; Shiwei Lan; Wesley O. Johnson; Radford M. Neal (Stat. Comput., 2014)

Inference for Belief Networks Using Coupling From the Past - Michael Harvey; Radford M. Neal (CoRR, 2013)

Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals - Chunyi Wang; Radford M. Neal (CoRR, 2012)

On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note) - Radford M. Neal (CoRR, 2011)

Nonlinear Models Using Dirichlet Process Mixtures - Babak Shahbaba; Radford M. Neal (J. Mach. Learn. Res., 2009)

Difference detection in LC-MS data for protein biomarker discovery - Jennifer Listgarten; Radford M. Neal; Sam T. Roweis; Peter Wong; Andrew Emili (Bioinform., 2007)

Pattern Recognition and Machine Learning - Radford M. Neal (Technometrics, 2007)

Gene function classification using Bayesian models with hierarchy-based priors - Babak Shahbaba; Radford M. Neal (BMC Bioinform., 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 (Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 4-7, 2006, 2006)

Modeling Dyadic Data with Binary Latent Factors - Edward Meeds; Zoubin Ghahramani; Radford M. Neal; Sam T. Roweis (Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 4-7, 2006, 2006)

High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees - Radford M. Neal; Jianguo Zhang (Feature Extraction - Foundations and Applications, 2006)

Classification with Bayesian Neural Networks - Radford M. Neal (Machine Learning Challenges, Evaluating Predictive Uncertainty, Visual Object Classification and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers, 2005)

Multiple Alignment of Continuous Time Series - Jennifer Listgarten; Radford M. Neal; Sam T. Roweis; Andrew Emili (Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, NIPS 2004, December 13-18, 2004, Vancouver, British Columbia, Canada], 2004)

Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models - Radford M. Neal; Matthew J. Beal; Sam T. Roweis (Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada], 2003)

Annealed importance sampling - Radford M. Neal (Stat. Comput., 2001)

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 (UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30 - July 3, 2000, 2000)

Arithmetic Coding Revisited - Alistair Moffat; Radford M. Neal; Ian H. Witten (ACM Trans. Inf. Syst., 1998)

Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation - Radford M. Neal (Learning in Graphical Models, 1998)

A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants - Radford M. Neal; Geoffrey E. Hinton (Learning in Graphical Models, 1998)

Factor Analysis Using Delta-Rule Wake-Sleep Learning - Radford M. Neal; Peter Dayan (Neural Comput., 1997)

Sampling from multimodal distributions using tempered transitions - Radford M. Neal (Stat. Comput., 1996)

Bayesian learning for neural networks - Radford M. Neal (University of Toronto, Canada, 1995)

The Helmholtz machine - Peter Dayan; Geoffrey E. Hinton; Radford M. Neal; Richard S. Zemel (Neural Comput., 1995)

Arithmetic Coding Revisited - Alistair Moffat; Radford M. Neal; Ian H. Witten (Proceedings of the IEEE Data Compression Conference, DCC 1995, Snowbird, Utah, USA, March 28-30, 1995, 1995)

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)

Connectionist Learning of Belief Networks - Radford M. Neal (Artif. Intell., 1992)

Asymmetric Parallel Boltzmann Machines are Belief Networks - Radford M. Neal (Neural Comput., 1992)

Bayesian Learning via Stochastic Dynamics - Radford M. Neal (Advances in Neural Information Processing Systems 5, [NIPS Conference, Denver, Colorado, USA, November 30 - December 3, 1992], 1992)

Arithmetic Coding for Data Compression - Ian H. Witten; Radford M. Neal; John G. Cleary (Commun. ACM, 1987)

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)