Robert L Wolpert

I'm a modeler-- I build computer-resident mathematical models for complex systems, and invent and program numerical algorithms for making inference from the models. Usually this involves predicting things that haven't been measured (yet). Always it involves managing uncertainty and making good decisions when some of the information we'd need to be fully comfortable in our decision-making is unknown.

Originally trained as a mathematician specializing in probability theory and stochastic processes, I was drawn to statistics by the interplay between theoretical and applied research- with new applications suggesting what statistical areas need theoretical development, and advances in theory and methodology suggesting what applications were becoming practical and so interesting. Through all of my statistical interests (theoretical, applied, and methodological) runs the unifying theme of the Likelihood Principle, a constant aid in the search for sensible methods of inference in complex statistical problems where commonly-used methods seem unsuitable. Three specific examples of such areas are:

Bayesian robustness, the search for reliable statistical methods when we harbor doubts about our data, our models, or our prior beliefs;
Meta-analysis, of how we can synthesize evidence of different sorts about a statistical problem; and
Nonparametric Bayesian analysis, for applications in which common parametric families of distributions seem unsuitable.

Many of the methods in common use in each of these areas are hard or impossible to justify, and can lead to very odd inferences that seem to misrepresent the statistical evidence. Many of the newer approaches abandon the ``iid'' paradigm in order to reflect patterns of regional variation, and nearly all of them depend on recent advances in the power of computer hardware and algorithms, leading to two other areas of interest:

Spatial Statistics, and
Statistical computation.

I have a special interest in developing statistical methods for application to problems in Environmental Science, where traditional methods often fail. Recent examples include the development of nonexchangeable hierarchical Bayesian models for synthesizing evidence about the health effects of environmental pollutants, in several separate applications, and the use of high-dimensional Bayesian models to reflect uncertainty in mechanistic environmental simulation models, in another.

My current (2005-2006) research involves modelling and Bayesian inference of dependent time series and (continuous-time) stochastic processes with jumps. Examples include work loads on networks of digital devices; peak heights in mass spectrometry experiments; or multiple pollutant levels at spatially and temporally distributed sites.