Radford M. Neal

• Bayesian learning for neural networks
• Annealed importance sampling
• Markov chain Monte Carlo methods based on "slicing" the density function
• Monte Carlo implementation of Gaussian process models for Bayesian regression and classification
• Sampling from multimodal distributions using tempered transitions