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.
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, 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.