Index: Robert B. Gramacy

• At UCSB in Fall I am teaching PSTAT 215A, a graduate course in Bayesian inference.
• At Cambridge in Lent I am teaching Part III/Mphil Time Series and Monte Carlo Inference.

My research interests include Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimizaton under uncertainty. My application areas of interest include spatial data, sequential computer experiments, ecology, epidemiology, finance and public policy.

I am supervising the following students:

• James Lawrence (2008): Sequential Monte Carlo methods for model selection in ecology.
• Tim Graves (2009): Detection of long-range dependencies in time series from nature.
• Adam Bull (2009): Sequential design for constrained optimization of a noisy function.

A postdoc, Ioana Cosma, is working with me on applying particle learning to covariance estimation for financial asset returns data.

See the full list.

• Particle learning of Gaussian process models for sequential design and optimization. (2009) with Nicholas Polson. Preprint on arXiv:0909.5262
• Shrinkage regression for multivariate inference with missing data, and an application to portfolio balancing. (2009) with Ester Pantaleo. Preprint on arXiv:0907.2135
• Adaptive Design and Analysis of Supercomputer Experiments. (2009) with Herbert K.H. Lee. Technometrics, 51(2), pp. 130-145; preprint on arXiv:0805.4359
• Importance tempering. (2008) with Richard Samworth, and Ruth King. To appear in Statistics and Computing; preprint on arXiv:0707.4242
• On estimating covariances between many assets with histories of highly variable length. (2008) with Joo Hee Lee and Ricardo Silva. Preprint on arXiv:0710.5837
• Bayesian treed Gaussian process models with an application to computer modeling. (2008) with Herbert K.H. Lee. Journal of the American Statistical Association, 103(483), pp. 1119-1130; preprint on arXiv:0710.4536