Statistics is not always easy. It raises many interesting philosophical issues, and often requires the solution of difficult computational problems. Here are some problems and solutions of particular interest to me (click on title for more details):

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 that are relevant to, or at least inspired by, the way learning and computation may occur in the brain.Latent variable models

Models phrased in terms of entities that we have invented to explain patterns we see in observable variables.Evaluation of learning methods

Ways of telling which methods for learning from data really work.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.Statistical applications

I have worked on various statistical applications, mostly of a biological nature.I also have current, dormant, or possible future interests in philosophy of science, artificial life, programming languages, user interface design, and who knows what else...

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