David Higdon

According to our database, David Higdon authored at least 26 papers between 1997 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

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

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2024

2023

Active Learning for Deep Gaussian Process Surrogates.

Annie Sauer, Robert B. Gramacy, David Higdon

Technometrics, January, 2023

2020

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

2019

Optimizing spatial allocation of seasonal influenza vaccine under temporal constraints.

Srinivasan Venkatramanan, Jiangzhuo Chen, Arindam Fadikar, Sandeep Gupta, 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 Discrepancy Models.

Brian de Silva, David M. Higdon, Steven L. Brunton, J. Nathan Kutz

CoRR, 2019

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

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

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

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

Proceedings of the 2017 Winter Simulation Conference, 2017

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

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

2016

Bayesian Additive Regression Tree Calibration of Complex High-Dimensional Computer Models.

Matthew T. Pratola, David M. Higdon

Technometrics, 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

A Bayesian simulation approach for supply chain synchronization.

Bianica Pires, Joshua Goldstein, David Higdon, Gizem Korkmaz, Sallie Keller, Stephanie Shipp, Ken Hamall, Art Koehler

Proceedings of the Winter Simulation Conference, 2016

2015

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

2014

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

Proceedings of the Dynamic Data-Driven Environmental Systems Science, 2014

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

Gaussian Process Modeling of Derivative Curves.

Tracy Holsclaw, Bruno Sansó, Herbert K. H. Lee, Katrin Heitmann, Salman Habib, David Higdon, Ujjaini Alam

Technometrics, 2013

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, Katrin Heitmann, Steve Price

Technometrics, 2013

2012

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

2006

Variable Selection for Gaussian Process Models in Computer Experiments.

Crystal Linkletter, Derek Bingham, Nicolas W. Hengartner, David Higdon, Kenny Q. Ye

Technometrics, 2006

Analysis of Multi-domain Complex Simulation Studies.

James R. Gattiker, Earl Lawrence, David Higdon

Proceedings of the Computational Science and Its Applications, 2006

2004

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

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

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

1999

Bayesian inference and Markov chain Monte Carlo in imaging.

David M. Higdon, James E. Bowsher

Proceedings of the Medical Imaging 1999: Image Processing, 1999

1997

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