My Research:

Publications

• Selection of a Representative Sample with Matthew Taddy and Genetha Gray (to appear in the Journal of Classification, 2010; this version is UCSC TR SOE-08-12)
• Bayesian Guided Pattern Search for Robust Local Optimization with Matthew Taddy, Genetha Gray, and Joshua Griffin (Technometrics, 2009, Volume 51, pp. 389-401; this version is UCSC TR ams2008-02)
• Fast Inference for Statistical Inverse Problems with Matthew Taddy and Bruno Sansó (Inverse Problems, 2009, Volume 25, 085001; also available is the older version, UCSC TR ams2008-03)
• Adaptive Design and Analysis of Supercomputer Experiments with Robert Gramacy (Technometrics, 2009, Volume 51, pp. 130-145; this version is UCSC TR ams2006-13)
• Bayesian Treed Gaussian Process Models with an Application to Computer Modeling with Robert Gramacy (Journal of the American Statistical Association, 2008, Volume 103, pp. 1119-1130)
• Inference for a Proton Accelerator Using Convolution Models with Bruno Sansó, Weining Zhou, and Dave Higdon (Journal of the American Statistical Association, 2008, Volume 103, pp. 604-613; this version is UCSC TR ams2005-31)
• Gaussian Processes and Limiting Linear Models with Robert Gramacy (Computational Statistics and Data Analysis, 2008, Volume 53, pp. 123-136)
• Chocolate Chip Cookies as a Teaching Aid (The American Statistician 2007, Volume 61, Issue 4, pp. 351-355)
• Multiscale Modeling: A Bayesian Perspective. With Marco Ferreira (2007)
• Default Priors for Neural Network Classification (Journal of Classification, 2007, pp. 53-70; this version is UCSC TR ams2005-15)
• Multi-resolution Genetic Algorithms and Markov Chain Monte Carlo with Chris Holloman and Dave Higdon (Journal of Computational and Graphical Statistics 2006, pp. 861-879; this version is Duke ISDS TR #02-06)
• Multi-Scale and Hidden Resolution Time Series Models with Marco A. R. Ferreira, Mike West, and Dave Higdon (Bayesian Analysis, 2006, pp. 947-968)
• Inferring Particle Distribution in a Proton Accelerator Experiment with Bruno Sansó, Weining Zhou, and Dave Higdon (Bayesian Analysis, 2006, pp. 249-264)
• Neural Networks and Default Priors (Proceedings of the American Statistical Association, Section on Bayesian Statistical Science, 2005)
• Efficient Models for Correlated Data via Convolutions of Intrinsic Processes with Dave Higdon, Kate Calder, and Chris Holloman (Statistical Modelling, 2005; this version is UCSC TR ams2004-03)
• Bayesian Nonparametrics via Neural Networks. (2004)
• Priors for Neural Networks (2004, in Classification, Clustering, and Data Mining Applications, pp. 141-150; this version is UCSC TR ams2003-09)
• Parameter Space Exploration With Gaussian Process Trees with Robert Gramacy and William Macready (2004, in Proceedings of the International Conference on Machine Learning, pp. 353-360)
• Lossless Online Bayesian Bagging with Merlise Clyde (Journal of Machine Learning Research, February 2004)
• Markov chain Monte Carlo-based approaches for inference in computationally intensive inverse problems with Dave Higdon and Chris Holloman (2003, in Bayesian Statistics 7, pp. 181-197; this version is Duke ISDS #02-10)
• Multi-scale Modeling of 1-D Permeability Fields with Marco A. R. Ferreira, Zhuoxin Bi, Mike West, and Dave Higdon (2003, in Bayesian Statistics 7, pp. 519-527; this version is Duke ISDS TR #02-08)
• A Noninformative Prior for Neural Networks (Machine Learning, 2003; this version is Duke ISDS TR #00-04)
• Markov Random Field Models for High-Dimensional Parameters in Simulations of Fluid Flow in Porous Media with David Higdon, Zhuoxin Bi, Marco Ferreira, and Mike West (Technometrics, August 2002; this version is Duke ISDS #00-35), a version of which also won Best Contributed Paper in the Statistical Computing Section sessions at the 2000 Joint Statistical Meetings
• A Bayesian Approach to Characterizing Uncertainty in Inverse Problems Using Coarse and Fine Scale Information with Dave Higdon and Zhuoxin Bi (IEEE Transactions on Signal Processing, February 2002; this version is Duke ISDS TR #01-02)
• Did Lennox Lewis Beat Evander Holyfield? Methods for Analyzing Small-sample Inter-rater Agreement Problems with Daniel Cork and David Algranati (The Statistician, July 2002; this version is CMU Stats TR #732)
• Difficulties in Estimating the Normalizing Constant of the Posterior for a Neural Network (Journal of Computational and Graphical Statistics, March 2002)
• Model Selection for Neural Network Classification (Journal of Classification, 2001; this version is Duke ISDS TR #00-18)
• Bagging and the Bayesian Bootstrap with Merlise Clyde (In Artificial Intelligence and Statistics 2001, T. Richardson and T. Jaakkola eds.; this version is Duke ISDS TR #00-34)
• Loglinear Models and Goodness-of-Fit Statistics for Train Waybill Data, with Kert Viele (Journal of Transportation and Statistics, April 2001)
• Consistency of Posterior Distributions for Neural Networks (Neural Networks, July 2000; this version is CMU Stats TR #676, 1998)
• Model Selection and Model Averaging for Neural Network Regression (Proceedings of the American Statistical Association, Section on Bayesian Statistical Science, 1999; this version is Duke ISDS TR #00-32)

Technical Reports

• Bayesian Treed Gaussian Process Models with Robert Gramacy (2006 - UCSC TR ams2006-08)
• Adaptive Exploration of Computer Experiment Parameter Spaces with Robert Gramacy and William Macready (2005 - UCSC TR ams2005-16)
• Multi-scale Random Field Models with Marco Ferreira, Dave Higdon, and Mike West (2005)
• Upscaling Tensorial Permeability Fields Based on Gaussian Markov Random Field Models and the Hybrid Mixed Finite Element Method with Zhuoxin Bi, John Trangenstein, and Dave Higdon (2005 -- UCSC TR ams2005-14)
• Flexible Gaussian Processes via Convolution with Chris Holloman, Kate Calder, and Dave Higdon (2002 -- Duke ISDS #02-09)
• Model Selection for Consumer Loan Application Data (1996 -- CMU Stats TR #650)