Fully Bayesian Estimation of Gibbs Hyperparameters for Emission Computed Tomography Data. - David M. Higdon; James E. Bowsher; Valen E. Johnson; Timothy G. Turkington; David R. Gilland; Ronald J. Jaszczak (IEEE Trans. Medical Imaging, 1997)

Bayesian inference and Markov chain Monte Carlo in imaging. - David M. Higdon; James E. Bowsher (Image Processing, 1999)

Markov Random Field Models for High-Dimensional Parameters in Simulations of Fluid Flow in Porous Media. - Herbert K. H. Lee; David M. Higdon; Zhuoxin Bi; Marco A. R. Ferreira; Mike West (Technometrics, 2002)

A Bayesian approach to characterizing uncertainty in inverse problems using coarse and fine-scale information. - Dave Higdon; Herbert K. H. Lee; Zhuoxin Bi (IEEE Trans. Signal Process., 2002)

Combining Field Data and Computer Simulations for Calibration and Prediction. - Dave Higdon; Marc C. Kennedy; James C. Cavendish; John A. Cafeo; Robert D. Ryne (SIAM J. Sci. Comput., 2004)

Analysis of Multi-domain Complex Simulation Studies. - James R. Gattiker; Earl Lawrence; David Higdon (ICCSA (3), 2006)

Variable Selection for Gaussian Process Models in Computer Experiments. - Crystal Linkletter; Derek Bingham; Nicolas W. Hengartner; David Higdon; Kenny Q. Ye (Technometrics, 2006)

Adaptive Hessian-Based Nonstationary Gaussian Process Response Surface Method for Probability Density Approximation with Application to Bayesian Solution of Large-Scale Inverse Problems. - Tan Bui-Thanh; Omar Ghattas; David Higdon (SIAM J. Sci. Comput., 2012)

Computer Model Calibration Using the Ensemble Kalman Filter. - David Higdon; James R. Gattiker; Earl Lawrence; Charles S. Jackson; Michael Tobis; Matthew T. Pratola; Salman Habib 002; Katrin Heitmann; Steve Price (Technometrics, 2013)

Gaussian Process Modeling of Derivative Curves. - Tracy Holsclaw; Bruno Sansó; Herbert K. H. Lee; Katrin Heitmann; Salman Habib 002; David Higdon; Ujjaini Alam (Technometrics, 2013)

Methods for Characterizing and Comparing Populations of Shock Wave Curves. - Curtis B. Storlie; Michael Fugate; David M. Higdon; Aparna V. Huzurbazar; Elizabeth G. Francois; Douglas C. McHugh (Technometrics, 2013)

Specification of the Ionosphere-Thermosphere Using the Ensemble Kalman Filter. - Humberto C. Godinez; Earl Lawrence; David Higdon; Aaron J. Ridley; Josef Koller; Alexei V. Klimenko (DyDESS, 2014)

Visualizing discrepancies from nonlinear models and computer experiments. - Brian P. Weaver; Richard L. Warr; Christine M. Anderson-Cook; David M. Higdon (Stat. Anal. Data Min., 2015)

A Bayesian simulation approach for supply chain synchronization. - Bianica Pires; Joshua Goldstein; David Higdon; Gizem Korkmaz; Sallie Keller; Stephanie Shipp; Ken Hamall; Art Koehler (WSC, 2016)

Using a Gaussian Process as a Nonparametric Regression Model. - James R. Gattiker; Michael S. Hamada; David Higdon; Matthias Schonlau; William J. Welch (Qual. Reliab. Eng. Int., 2016)

Bayesian Additive Regression Tree Calibration of Complex High-Dimensional Computer Models. - Matthew T. Pratola; David M. Higdon (Technometrics, 2016)

Epidemic Forecasting Framework Combining Agent-Based Models and Smart Beam Particle Filtering. - Farzaneh Tabataba; Bryan L. Lewis; Milad Hosseinipour; Foroogh S. Tabataba; Srinivasan Venkatramanan; Jiangzhuo Chen; Dave Higdon; Madhav V. Marathe (ICDM, 2017)

A Bayesian simulation approach for supply chain synchronization. - Bianica Pires; Joshua Goldstein; Dave Higdon; Paul Sabin; Gizem Korkmaz; Stephanie Shipp; Sallie Keller; Shan Ba; Ken Hamall; Art Koehler; Shane Reese (WSC, 2017)

Joining statistics and geophysics for assessment and uncertainty quantification of three-dimensional seismic Earth models. - Carène Larmat; Monica Maceira; David M. Higdon; Dale N. Anderson (Stat. Anal. Data Min., 2017)

Estimating individualized exposure impacts from ambient ozone levels: A synthetic information approach. - Bianica Pires; Gizem Korkmaz; Katherine B. Ensor; David Higdon; Sallie Keller; Bryan L. Lewis; Aaron D. Schroeder (Environ. Model. Softw., 2018)

Calibrating a Stochastic, Agent-Based Model Using Quantile-Based Emulation. - Arindam Fadikar; Dave Higdon; Jiangzhuo Chen; Bryan L. Lewis; Srinivasan Venkatramanan; Madhav V. Marathe (SIAM/ASA J. Uncertain. Quantification, 2018)

Discovery of Physics from Data: Universal Laws and Discrepancy Models. - Brian de Silva; David M. Higdon; Steven L. Brunton; J. Nathan Kutz (CoRR, 2019)

Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints. - Srinivasan Venkatramanan; Jiangzhuo Chen; Arindam Fadikar; Sandeep Gupta 004; Dave Higdon; Bryan L. Lewis; Madhav V. Marathe; Henning S. Mortveit; Anil Vullikanti (PLoS Comput. Biol., 2019)

Discovery of Physics From Data: Universal Laws and Discrepancies. - Brian de Silva; David M. Higdon; Steven L. Brunton; J. Nathan Kutz (Frontiers Artif. Intell., 2020)

Active Learning for Deep Gaussian Process Surrogates. - Annie Sauer; Robert B. Gramacy; David Higdon (Technometrics, 2023)

Towards Improved Uncertainty Quantification of Stochastic Epidemic Models Using Sequential Monte Carlo. - Arindam Fadikar; Abby Stevens; Nicholson T. Collier; Kok Ben Toh; Olga Morozova; Anna Hotton; Jared Clark; David Higdon; Jonathan Ozik (IPDPS (Workshops), 2024)

Towards Foundation Models: Evaluation of Geoscience Artificial Intelligence with Uncertainty. - Samuel Myren; Nidhi Parikh; Rosalyn Rael; Garrison Flynn; Dave Higdon; Emily Casleton (CoRR, 2025)