**Publications**

- Bayesian Nonstationary Gaussian Process Models via Treed Process
Convolutions with Waley Liang (
*to appear in Advances in Data Analysis and Classification*, 2019; this version is TR UCSC-SOE-11-25) - Real-Time Detection Of In-Flight Aircraft Damage
with Brenton Blair and Misty Davies (
*Journal of Classification*, 2017, Volume 34, Number 3, pp. 494-523; this version is TR UCSC-SOE-16-03) - Graphical Jump
Method for Neural Networks with Jing Chang (
*Journal of Data Science*, 2017, Volume 15, Number 4, pp. 669-690) - A Robust Algorithm for Optimum Utility with John Guenther (
*Journal of Advances in Applied Mathematics*, 2017, Volume 2, Number 1, pp. 1-14) - Cluster Search Algorithm for Finding Multiple Optima with John Guenther (
*Applied Mathematics*, 2016, Volume 7, Number 7, pp. 736-752) -
Multivariate Stochastic Process Models for Correlated Responses of Mixed Type with Tony Pourmohamad (
*Bayesian Analysis*, 2016, Volume 11, Number 3, pp. 797-820) - Modeling an Augmented Lagrangian for
Blackbox Constrained Optimization (with discussion) with Robert Gramacy, Genetha
Gray, Sebastien Le Digabel, Pritam Ranjan, Garth Wells, and Stefan Wild
(
*Technometrics*, Volume 58, Issue 1, pp. 1-11) - Sequential Design for Achieving Estimated Accuracy of Global Sensitivities
with John Guenther and Genetha Gray (
*Applied Stochastic Models in Business and Industry*, 2015, Volume 31, Issue 6, pp. 782-800; this version is TR 2013 - TR UCSC-SOE-13-17) - Variable Selection via a
Multi-stage Strategy with Jing Chang (
*Journal of Applied Statistics*, 2015, Volume 42, Number 4, pp. 762-774) - Optimization Under Constraints by Applying an Asymmetric Entropy
Measure with David Lindberg (
*Journal of Computational and Graphical Statistics*, 2015, Volume 24, Number 2, pp. 379-393; this version is TR UCSC-SOE-13-06) - Sequential Process Convolution Gaussian Process Models via Particle Learning with Waley Liang (
*Statistics and Its Interface*, 2014, Volume 7, Number 4, pp. 465-475; this version is TR UCSC-SOE-12-09) - Finding and Choosing Among Multiple Optima with John Guenther and
Genetha Gray (
*Applied Mathematics*, 2014, Volume 5, Number 2, pp. 300-317) - Modeling and Anomalous Cluster Detection for Point Processes Using Process Convolutions with Waley Liang,
Jacob Colvin, and Bruno Sansó (
*Journal of Computational and Graphical Statistics*, 2014, Volume 23, Number 1, pp. 129-150; this version is TR UCSC-SOE-10-09) - Gaussian Process Modeling of Derivative Curves with Tracy Holsclaw,
Bruno Sansó, David Higdon, Katrin Heitmann, Ujjaini Alam, and
Salman Habib (to appear in
*Technometrics*, 2013; this version is TR UCSC-SOE-11-02) -
Simultaneous Optimization and Uncertainty Quantification with
Genetha Gray and John Guenther (
*Journal of Computational Methods in Sciences and Engineering*, 2012, Volume 12, Number 1-2, pp. 99-110) -
Bagging During Markov Chain Monte Carlo for Smoother Predictions
(
*Antarctica Journal of Mathematics*, 2013, Volume 10, Number 5, Paper 4, pp. 447-451), an earlier version is TR UCSC-SOE-13-09 -
Cases for the Nugget in Modeling Computer Experiments with
Robert Gramacy (
*Statistics and Computing*, 2012, Volume 22, pp. 713-722) - Optimization Subject to Hidden Constraints via Statistical
Emulation with Robert Gramacy, Crystal Linkletter, and Genetha
Gray (
*Pacific Journal of Optimization*, 2011, Volume 7, pp. 467-478; this version is TR UCSC-SOE-10-10) - Nonparametric Reconstruction of
the Dark Energy Equation of State from Diverse Data Sets with
Tracy Holsclaw, Bruno Sansó, David Higdon, Katrin Heitmann,
Ujjaini Alam, and Salman Habib (
*Physical Review D*, 2011, Volume 82, 083501) -
Optimization Under Unknown Constraints with Robert Gramacy
(2011, in
*Bayesian Statistics 9*with discussion, pp. 229-256) - Exploring the Effect of Weight Misspecification on Flight Prediction,
with Jing Chang (
*Advances and Applications in Statistical Sciences*, 2011, Volume 6, Issue 1, pp. 27-38) -
Designing and Analyzing a Circuit Device Experiment Using
Treed Gaussian Processes,
with Matthew Taddy, Robert Gramacy, and Genetha
Gray, in
*The Oxford Handbook of Applied Bayesian Analysis*, 2010 -
Nonparametric Dark Energy Reconstruction from Supernova Data
with Tracy Holsclaw, Ujjaini Alam, Bruno Sansó, Katrin Heitmann,
Salman Habib and David Higdon (
*Physical Review Letters*, 2010, Volume 105, 241302) -
Nonparametric Reconstruction of the Dark Energy Equation of
State with Tracy Holsclaw, Ujjaini Alam, Bruno Sansó, Katrin
Heitmann, Salman Habib and David Higdon (
*Physical Review D*, 2010, Volume 82, 103502) - Selection of a Representative Sample
with Matthew Taddy and Genetha Gray (
*Journal of Classification*, 2010, Volume 27, pp. 41-53; 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) - 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**

- The Statistical Filter Approach to Constrained Optimization with Tony Pourmohamad (2018 - TR UCSC-SOE-18-14)
- Determining Convergence in Gaussian Process Surrogate Model Optimization with Nick Grunloh (2015 - TR UCSC-SOE-15-05)
- Predicting Variable-Length Functional Outputs for Emulation of a Flight Simulator with Yuning He and Misty Davies (2012 - TR UCSC-SOE-12-16)
- 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 -- Duke ISDS #05-02)
- 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)

Last modified on January 21, 2019.