Slides From Talks by Radford Neal

These slides are mostly availabe in both PostScript and PDF formats.

• ``Probability and Anthropic Reasoning in Small, Large, and Infinite Universes'', Conference on Challenges for Early Universe Cosmology, Perimeter Institute, July 2011: PDF of slides (but note that much of the talk was on the blackboard).
• ``New Monte Carlo Methods Based on Hamiltonian Dynamics'', MaxEnt 2011, July 2011: PDF.
• ``MCMC Using Ensembles of States with Application to Gaussian Process Regression'', talk given at the University of Toronto Dept. of Economics (2010-10-22) and Dept. of Computer Science (2010-11-15): Postscript, PDF.
• ``Nuisance Parameters and Other Issues in Searching for Signals in High-Energy Physics Experiments'', Talk (remotely) at the PHYSTAT-LHC Workshop , June 2007: Postscript, PDF.
• ``Short-Cut MCMC: An Alternative to Adaptation'', Talk at the Third Workshop on Monte Carlo Methods, May 2007: Postscript, PDF.
• ``Constructing Efficient MCMC Methods Using Temporary Mapping and Caching'', Talk at Columbia University, December 2006: Postscript, PDF.
• ``Estimation of Failure Probabilities Using Linked Importance Sampling'', Workshop on Nonlinearlity and Randomness in Complex Systems, SUNY at Buffalo, April 2006: Postscript (0.4 MBytes), PDF (4.7 MBytes).
• ``Hamiltonian Importance Sampling'', BIRS workshop on Mathematical Issues in Molecular Dynamics, June 2005: Postscript, PDF.
• ``Creating Non-Gaussian Processes from Gaussian Processes by the Log-Sum-Exp Approach'', talk to the U of T machine learning group, February 2005: Postscript, PDF.
• NIPS*2004 tutorial on ``Bayesian Methods for Machine Learning'': Postscript, PDF.
• ``A New Proof of Peksun's Theorem Regarding the Asymptotic Variance of MCMC Estimators'', poster at ISBA 2004, Vina del Mar, Chile, May 2004: postscript, pdf.
• ``Classification for High Dimensional Problems Using Bayesian Neural Networks and Dirichlet Diffusion Trees'', NIPS*2003 Feature Selection Workshop, Whistler, British Columbia, December 2003 (describing the winning entry): pdf.
• ``Markov chain Monte Carlo computations for Dirichlet diffusion trees'', NTOC 2001, Kyoto, December 2001: postscript, pdf.
• ``Survival analysis using a Bayesian neural network'', Joint Statistical Meetings, Atlanta, 2001: postscript, pdf.
• ``Monte Carlo decoding of LDPC codes'', ICTP Workshop on Statistical Physics and Capacity-Approaching Codes, May 2001: postscript, pdf.
• ``Circularly-coupled Markov chain sampling'', April 2000: postscript, pdf. See also the earlier technical report.
• ``Markov chain sampling using Hamiltonian dynamics'', Joint Statistical Meetings, Baltimore, 1999: postscript, pdf.
• ``Faster encoding for low-density parity check codes using sparse matrix methods'', IMA workshop on Codes, Systems and Graphical Models, Minneapolis, 1999: postscript, pdf.
• ``Tutorial on exact sampling methods'', given 26 October 1998 during the workshop on Monte Carlo methods at the Fields Institute: postscript, pdf.
• ``Improving Markov chain sampling by suppressing random walks'', AMS-IMS-SIAM Joint Workshop on Stochastic Inference, Monte Carlo, and Empirical Methods, July 1996: postscript, pdf.