Papers by Radford Neal Available On-Line

This is a complete list of papers I have available on-line, in PostScript and PDF formats. They are listed in reverse chronological order, except for a few old versions, for which there are links in the "abstract" pages. Some of the papers here are also available on-line at http://arXiv.org, as noted on their abstact pages.

Abstracts for most other papers are available on-line, accessible from my publications list, or the summary of my research interests.

There are also some slides from talks and miscellaneous other documents available.


Shestopaloff, A. Y. and Neal, R. M. (2016) ``Sampling latent states for high-dimensional non-linear state space models with the embedded HMM method'', Technical Report, 21 pages: abstract, pdf.

Neal, R. M. (2015) ``Fast exact summation using small and large superaccumulators'', Technical Report, 22 pages: abstract, pdf, associated software.

Neal, R. M. (2015) ``Representing numeric data in 32 bits while preserving 64-bit precision'', Technical Report, 16 pages: abstract, pdf, associated software.

Shestopaloff, A. Y. and Neal, R. M. (2014) ``Efficient Bayesian inference for stochastic volatility models with ensemble MCMC methods'', Technical Report, 17 pages: abstract, pdf.

Shestopaloff, A. Y. and Neal, R. M. (2013) ``On Bayesian inference for the M/G/1 queue with efficient MCMC sampling'', Technical Report, 18 pages: abstract, pdf.

Wang, C. and Neal, R. M. (2013) ``MCMC methods for Gaussian process models using fast approximations for the likelihood'', Technical Report, 21 pages: abstract, pdf.

Shestopaloff, A. Y. and Neal, R. M. (2013) ``MCMC for non-linear state space models using ensembles of latent sequences'', Technical Report, 18 pages: abstract, pdf.

Wang, C. and Neal, R. M. (2012) ``Gaussian process regression with heteroscedastic or non-Gaussian residuals'', Technical Report, 19 pages: abstract, pdf.

Neal, R. M. (2012) ``How to View an MCMC Simulation as a Permutation, with Applications to Parallel Simulation and Improved Importance Sampling'', Technical Report No. 1201, Dept. of Statistics, University of Toronto, 42 pages: abstract, postscript, pdf.

Neal, R. M. (2010) ``MCMC using ensembles of states for problems with fast and slow variables such as Gaussian process regression'', Technical Report No. 1011, Dept. of Statistics, University of Toronto, 24 pages: abstract, postscript, pdf, associated software.

Thompson, M. B. and Neal, R. M. (2010) ``Slice sampling with adaptive multivariate steps: The shrinking-rank method'', JSM 2010, Section on Statistical Computing, pp. 3890-3896: abstract, pdf.

Thompson, M. and Neal, R. M. (2010) ``Covariance-adaptive slice sampling'', Technical Report No. 1002, Dept. of Statistics, University of Toronto, 17 pages: abstract, postscript, pdf.

Neal, R. M. (2010) ``MCMC using Hamiltonian dynamics'', to appear in the Handbook of Markov Chain Monte Carlo, S. Brooks, A. Gelman, G. Jones, and X.-L. Meng (editors), Chapman & Hall / CRC Press, 51 pages: abstract, postscript, pdf, associated software.

Shahbaba, B. and Neal, R. M. (2009) ``Nonlinear Models Using Dirichlet Process Mixtures'', Journal of Machine Learning Research, vol. 10, pp. 1829-1850: abstract, pdf, associated references.

Li, L. and Neal, R. M. (2008) ``Compressing parameters in Bayesian high-order models with application to logistic sequence models'', Bayesian Analysis, vol. 3, pp. 793-822: abstract, pdf.

Neal, R. M. (2008) ``Computing likelihood functions for high-energy physics experiments when distributions are defined by simulators with nuisance parameters'', in the proceedings of the PHYSTAT-LHC Workshop on Statistical Issues for LHC Physics, June 2007, CERN 2008-001, pp. 119-126: abstract, postscript, pdf.

Li, L., Zhang, J., and Neal, R. M. (2008) ``A method for avoiding bias from feature selection with application to naive Bayes classification models'', Bayesian Analysis, vol. 3, pp. 171-196: abstract, pdf.

Shahbaba, B. and Neal, R. M. (2007) ``Nonlinear Models Using Dirichlet Process Mixtures'', Technical Report No. 0707, Dept. of Statistics, University of Toronto, 16 pages: abstract, postscript, pdf.

Li, L., Zhang, J., and Neal, R. M. (2007) ``A Method for Avoiding Bias from Feature Selection with Application to Naive Bayes Classification Models'', Technical Report No. 0705, Dept. of Statistics, University of Toronto, 21 pages: abstract, postscript, pdf.

Shahbaba, B. and Neal, R. M. (2006) ``Gene function classification using Bayesian models with hierarchy-based priors'', BMC Bioinformatics, 7:448, 9 pages: abstract, pdf, html, associated references.

Neal, R. M. (2006) ``Puzzles of anthropic reasoning resolved using full non-indexical conditioning'', Technical Report No. 0607, Dept. of Statistics, University of Toronto, 53 pages: abstract, postscript, pdf.

Shahbaba, B. and Neal, R. M. (2006) ``Gene function classification using Bayesian models with hierarchy-based priors'', Technical Report No. 0606, Dept. of Statistics, University of Toronto, 14 pages: abstract, postscript, pdf, associated references.

Neal, R. M. (2005) ``Estimating ratios of normalizing constants using Linked Importance Sampling'', Technical Report No. 0511, Dept. of Statistics, University of Toronto, 37 pages: abstract, postscript, pdf, associated software.

Shahbaba, B. and Neal, R. M. (2005) ``Improving classification when a class hierarchy is available using a hierarchy-based prior'', Technical Report No. 0510, Dept. of Statistics, University of Toronto, 11 pages: abstract, postscript, pdf, associated references.

Jain, S. and Neal, R. M. (2005) ``Splitting and merging components of a nonconjugate Dirichlet process mixture model'', Technical Report No. 0507, Dept. of Statistics, University of Toronto, 37 pages: abstract, postscript, pdf, associated references.

Neal, R. M. (2005) ``The short-cut Metropolis method'', Technical Report No. 0506, Dept. of Statistics, University of Toronto, 28 pages: abstract, postscript, pdf, associated software.

Listgarten, J., Neal, R. M., Roweis, S. T., and Emili, A. (2005) ``Multiple alignment of continuous time series'', in L. K. Saul, et al (editors), Advances in Neural Information Processing Systems 17 (aka NIPS*2004), MIT Press, 8 pages: abstract, postscript, pdf.

Neal, R. M. (2004) ``Taking bigger Metropolis steps by dragging fast variables'', Technical Report No. 0411, Dept. of Statistics, University of Toronto, 9 pages: abstract, postscript, pdf, associated software.

Neal, R. M. (2004) ``Improving asymptotic variance of MCMC estimators: Non-reversible chains are better'', Technical Report No. 0406, Dept. of Statistics, University of Toronto, 25 pages: abstract, postscript, pdf.

Neal, R. M., Beal, M. J., and Roweis, S. T. (2004) ``Inferring state sequences for non-linear systems with embedded hidden Markov models'', in S. Thrun, et al (editors), Advances in Neural Information Processing Systems 16 (aka NIPS*2003), MIT Press, 8 pages: abstract, postscript, pdf, associated reference.

Neal, R. M. (2003) ``Markov chain sampling for non-linear state space models using embedded hidden Markov models'', Technical Report No. 0304, Dept. of Statistics, University of Toronto, 9 pages: abstract, postscript, pdf.

Neal, R. M. (2003) ``Density modeling and clustering using Dirichlet diffusion trees'', in J. M. Bernardo, et al. (editors) Bayesian Statistics 7, pp. 619-629: abstract, postscript, pdf, associated references, associated software.

Neal, R. M. (2002) ``Circularly-coupled Markov chain sampling'', Technical Report No. 9910 (revised), Dept. of Statistics, University of Toronto, 49 pages: abstract, postscript, pdf.

Neal, R. M. (2001) ``Transferring prior information between models using imaginary data'', Technical Report No. 0108, Dept. of Statistics, University of Toronto, 29 pages: abstract, postscript, pdf, associated software.

Neal, R. M. (2001) ``Defining priors for distributions using Dirichlet diffusion trees'', Technical Report No. 0104, Dept. of Statistics, University of Toronto, 25 pages: abstract, postscript, pdf, associated software.

Pinto, R. L. and Neal, R. M. (2001) ``Improving Markov chain Monte Carlo estimators by coupling to an approximating chain'', Technical Report No. 0101, Dept. of Statistics, University of Toronto, 13 pages: abstract, postscript, pdf.

Neal, R. M. (2000) ``Slice sampling'', Technical Report No. 2005, Dept. of Statistics, University of Toronto, 40 pages: abstract, postscript, pdf, associated references, associated software.

Jain, S. and Neal, R. M. (2000) ``A Split-Merge Markov Chain Monte Carlo Procedure for the Dirichlet Process Mixture Model'', Technical Report No. 2003, Dept. of Statistics, University of Toronto, 32 pages: abstract, postscript, pdf, associated references.

Harvey, M. and Neal, R. M. (2000) ``Inference for Belief Networks Using Coupling From the Past'', in C. Boutilier and M. Goldszmidt (editors), Uncertainty in Artificial Intelligence: Proceedings of the Sixteenth Conference (2000), pp. 256-263: abstract, postscript, pdf, associated reference.

Neal, R. M. (2000) ``On deducing conditional independence from d-separation in causal graphs with feedback'', Journal of Artificial Intelligence Research, vol. 12, pp. 87-91: abstract, postscript, pdf.

Neal, R. M. (1999) ``Erroneous results in `Marginal likelihood from the Gibbs output''', unpublished letter: abstract, postscript, pdf.

Neal, R. M. (1998) ``Markov chain sampling methods for Dirichlet process mixture models'', Technical Report No. 9815, Dept. of Statistics, University of Toronto, 17 pages: abstract, postscript, pdf, associated software.

Neal, R. M. (1998) ``Assessing relevance determination methods using DELVE'', in C. M. Bishop (editor), Neural Networks and Machine Learning, pp. 97-129, Springer-Verlag: abstract, associated references, postscript, pdf.

Neal, R. M. (1998) ``Regression and classification using Gaussian process priors'' (with discussion), in J. M. Bernardo, et al (editors) Bayesian Statistics 6, Oxford University Press, pp. 475-501: abstract, postscript (without discussion), pdf (without discussion), associated reference, associated software.

Neal, R. M. (1998) ``Annealed importance sampling'', Technical Report No. 9805 (revised), Dept. of Statistics, University of Toronto, 25 pages: abstract, associated references, postscript, pdf.

Neal, R. M. and Hinton, G. E. (1998) ``A view of the EM algorithm that justifies incremental, sparse, and other variants'', in M. I. Jordan (editor) Learning in Graphical Models, pp. 355-368, Dordrecht: Kluwer Academic Publishers: abstract, postscript, pdf.

Neal, R. M. (1998) ``Suppressing random walks in Markov chain Monte Carlo using ordered overrelaxation'', in M. I. Jordan (editor) Learning in Graphical Models, pp. 205-225, Dordrecht: Kluwer Academic Publishers: abstract, associated references, postscript, pdf.

Neal, R. M. (1997) ``Markov chain Monte Carlo methods based on `slicing' the density function'', Technical Report No. 9722, Dept. of Statistics, University of Toronto, 27 pages: abstract, postscript, pdf, associated references.

Diaconis, P., Holmes, S., and Neal, R. M. (1997) ``Analysis of a non-reversible Markov chain sampler'', Technical Report BU-1385-M, Biometrics Unit, Cornell University, 26 pages: abstract, postscript, pdf.

Neal, R. M. (1997) ``Monte Carlo implementation of Gaussian process models for Bayesian regression and classification'', Technical Report No. 9702, Dept. of Statistics, University of Toronto, 24 pages: abstract, postscript, pdf, associated software.

Neal, R. M. and Dayan, P. (1996) ``Factor analysis using delta-rule wake-sleep learning'', Technical Report No. 9607, Dept. of Statistics, University of Toronto, 23 pages: abstract, associated references, postscript, pdf, associated software.

Neal, R. M. (1995) ``Suppressing random walks in Markov chain Monte Carlo using ordered overrelaxation'', Technical Report No. 9508, Dept. of Statistics, University of Toronto, 22 pages: abstract, associated reference, postscript, pdf.

Neal, R. M. (1994) Bayesian Learning for Neural Networks, Ph.D. Thesis, Dept. of Computer Science, University of Toronto, 195 pages: abstract, postscript, pdf, associated references, associated software.

Neal, R. M. (1994) ``Sampling from multimodal distributions using tempered transitions'', Technical Report No. 9421, Dept. of Statistics, University of Toronto, 22 pages: abstract, associated references, postscript, pdf.

Neal, R. M. (1994) ``Priors for infinite networks'', Technical Report CRG-TR-94-1, Dept. of Computer Science, University of Toronto, 22 pages: abstract, associated reference, postscript, pdf.

Neal, R. M. (1993) Probabilistic Inference Using Markov Chain Monte Carlo Methods, Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto, 144 pages: abstract, contents, postscript, pdf.

Neal, R. M. (1993) ``Bayesian learning via stochastic dynamics'', in C. L. Giles, S. J. Hanson, and J. D. Cowan (editors) Advances in Neural Information Processing Systems 5 (aka NIPS*1992), pp. 475-482, San Mateo, California: Morgan Kaufmann: abstract, postscript, pdf, associated references.

Neal, R. M. (1992) ``Bayesian training of backpropagation networks by the hybrid Monte Carlo method'', Technical Report CRG-TR-92-1, Dept. of Computer Science, University of Toronto, 21 pages: abstract, associated references, postscript, pdf.

Neal, R. M. (1991) ``Bayesian mixture modeling by Monte Carlo simulation'', Technical Report CRG-TR-91-2, Dept. of Computer Science, University of Toronto, 23 pages: abstract, associated reference, postscript, pdf.

Neal, R. M. (1990) ``Learning stochastic feedforward networks'', Technical Report CRG-TR-90-7, Dept. of Computer Science, University of Toronto, 34 pages: abstract, associated reference, postscript, pdf.

Neal, R. M. (1989) ``The computational complexity of taxonomic inference'', unpublished manuscript, 18 pages: postscript, pdf.


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