Radford Neal's Research Interests

Bayesian inference
An approach to statistics in which all forms of uncertainty are expressed in terms of probability.
Markov chain Monte Carlo
A way of computing high-dimensional integrals that is crucial for doing Bayesian inference.
Neural networks
Statistical models inspired by the way learning and computation may occur in the brain.
Latent variable models
Statistical models phrased in terms of entities that we have invented to explain patterns we see in observable variables.
Statistical / scientific computation
Efficient and accurate computation for statistical and scientific applications.
Evaluation of learning methods
Ways of telling which methods for learning from data really work.
Statistical applications
I have worked on various statistical applications, mostly of a biological nature.
Data compression
Using models for data to find a compressed representation of it.
Error correcting codes
Representing information in a redundant form that allows errors to be corrected with high probability.
Philosophy of scientific inference
The quest for a fundamental understanding of how we can come to justified conclusions about the world.
I also have current, dormant, or possible future interests in artificial life, programming languages, user interface design, and who knows what else...

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