Radford Neal's Research: Bayesian Inference

Bayesian inference is an approach to statistics in which all forms of uncertainty are expressed in terms of probability. I consider myself to be a rather "pure", but nevertheless "pragmatic" Bayesian, as described in my brief exposition of the philosophy of Bayesian inference.

For an introduction to Bayesian methods, you can look at the slides for my NIPS*2004 tutorial: Postscript or PDF.

Here are some papers that touch on fundamental issues in Bayesian inference:

Wang, C. and Neal, R. M. (2012) ``Gaussian process regression with heteroscedastic or non-Gaussian residuals'', Technical Report, 19 pages: abstract, pdf.
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.
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. (2005) ``Improving classification when a class hierarchy is available using a hierarchy-based prior'', Technical Report No. 0510, Dept. of Statistics, 11 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.
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 reference, associated software.
Neal, R. M. (1996) Bayesian Learning for Neural Networks, Lecture Notes in Statistics No. 118, New York: Springer-Verlag: blurb, associated references, associated software.
Neal, R. M. (1994) ``Priors for infinite networks'', Technical Report CRG-TR-94-1, Dept. of Computer Science, University of Toronto, 22 pages: abstract, postscript, pdf, associated reference.
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. (1991) ``Bayesian mixture modeling by Monte Carlo simulation'', Technical Report CRG-TR-91-2, Dept. of Computer Science, University of Toronto, 23 pages: abstract, postscript, pdf, associated references.

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