## Overview

I'm a stochastic 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:

- Computer modeling, the construction and analysis of fast small Bayesian

statistical emulators for big slow simulation models; - 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 abandon familiar (e.g. Gaussian) distributions in order to

reflect the heavier tails observed in realistic data, and nearly all of

them depend on recent advances in the power of computer hardware and

algorithms, leading to three other areas of interest:

- Spatial Statistics,
- Statistical Extremes, 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 developing new and better ways to estimate the

mortality to birds and bats from encounters with wind turbines; the

development of nonexchangeable hierarchical Bayesian models for

synthesizing evidence about the health effects of environmental pollutants;

and the use of high-dimensional Bayesian models to reflect uncertainty in

mechanistic environmental simulation models.

My current 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), problems arising in astrophysics (Gamma ray

bursts) and high-energy physics (heavy ion collisions), and the statistical

modelling of risk from, e.g., volcanic eruption.