David Higdon

发表

Analysis of Multi-domain Complex Simulation Studies

James R. Gattiker, Earl Lawrence, David Higdon, 2006, ICCSA.

A Primer on Space-Time Modeling from a Bayesian Perspective

David Higdon, D. Higdon, 2006 .

Multiscale Data Integration Using Markov Random Fields

Akhil Datta-Gupta, David Higdon, Sang Heon Lee, 2000 .

Flexible Gaussian Processes via Convolution

David Higdon, Herbert K. H. Lee, Catherine A. Calder, 2002 .

Introduction to Uncertainty Quantification

Houman Owhadi, Roger Ghanem, David Higdon, 2016 .

Posterior Exploration for Computationally Intensive Forward Models

Colin Fox, J. David Moulton, David Higdon, 2011 .

Adaptive Hessian-Based Nonstationary Gaussian Process Response Surface Method for Probability Density Approximation with Application to Bayesian Solution of Large-Scale Inverse Problems

David Higdon, Omar Ghattas, Tan Bui-Thanh, 2012, SIAM J. Sci. Comput..

Handbook of Uncertainty Quantification

Houman Owhadi, David Higdon, Roger Ghanem, 2017 .

Gaussian Process Modeling of Derivative Curves

David Higdon, Katrin Heitmann, Herbert K. H. Lee, 2013, Technometrics.

Integrating Computer and Physical Experiment Data

David Higdon, Brian J. Williams, 2014 .

A Bayesian hierarchical model to predict benthic oxygen demand from organic matter loading in estuaries and coastal zones

David Higdon, Mark E. Borsuk, Craig A. Stow, 2001 .

Homes from housing: a study of residential change and aspirations

David Higdon, Peter Kellet, 1991 .

Analyzing Stochastic Computer Models: A Review with Opportunities

Anirban Mondal, Robert B. Gramacy, Vadim Sokolov, 2020, Statistical Science.

Modeling and diagnosis of structural systems through sparse dynamic graphical models

Charles R. Farrar, Luke Bornn, David Higdon, 2016 .

Computer Experiments: Prediction Accuracy, Sample Size and Model Complexity Revisited

David Higdon, Derek Bingham, Ofir Harari, 2017 .

Variable Selection for Gaussian Process Models in Computer Experiments

Kenny Q. Ye, David Higdon, Derek Bingham, 2006, Technometrics.

Stochastic Simulators: An Overview with Opportunities

Anirban Mondal, Robert B. Gramacy, Vadim Sokolov, 2020 .

Science of Geological Carbon Sequestration: Integration of Experimentation and Simulation.

Rajesh J. Pawar, David Higdon, M. L. Hall, 2003 .

A process-convolution approach to modelling temperatures in the North Atlantic Ocean

David Higdon, D. Higdon, 1998, Environmental and Ecological Statistics.

Estimating individualized exposure impacts from ambient ozone levels: A synthetic information approach

David Higdon, Aaron Schroeder, Aaron D. Schroeder, 2018, Environ. Model. Softw..

[Bayesian Computation and Stochastic Systems]: Rejoinder

Kerrie Mengersen, Peter Green, David Higdon, 1995 .

Non-Stationary Spatial Modeling

David Higdon, John C. Kern, Jenise L. Swall, 2022, 2212.08043.

Active Learning for Deep Gaussian Process Surrogates.

Robert B. Gramacy, David Higdon, Annie Sauer, 2020 .

Using a Gaussian Process as a Nonparametric Regression Model

James R. Gattiker, David Higdon, Michael S. Hamada, 2016, Qual. Reliab. Eng. Int..

Inference for a Proton Accelerator Using

David Higdon, Herbert K. H. Lee, Weining Zhou, 2016 .

Specification of the Ionosphere-Thermosphere Using the Ensemble Kalman Filter

Earl Lawrence, David Higdon, Humberto C. Godinez, 2014, DyDESS.

Computer Model Calibration Using the Ensemble Kalman Filter

James R. Gattiker, Earl Lawrence, David Higdon, 2012, Technometrics.

Analyzing Stochastic Computer Models: A Review with Opportunities

Anirban Mondal, Robert B. Gramacy, Vadim Sokolov, 2020, Statistical Science.

Active Learning for Deep Gaussian Process Surrogates

Robert B. Gramacy, David Higdon, Annie Sauer, 2020, Technometrics.

Nonparametric Reconstruction of the Dark Energy Equation of State

David Higdon, Katrin Heitmann, Bruno Sanso, 2010, 1009.5443.

Cosmic calibration: Constraints from the matter power spectrum and the cosmic microwave background

David Higdon, Katrin Heitmann, Salman Habib, 2007 .

Simulation-Aided Inference in Cosmology

Earl Lawrence, David Higdon, Katrin Heitmann, 2012 .

THE COYOTE UNIVERSE. III. SIMULATION SUITE AND PRECISION EMULATOR FOR THE NONLINEAR MATTER POWER SPECTRUM

Earl Lawrence, David Higdon, Katrin Heitmann, 2009, 0912.4490.

THE COYOTE UNIVERSE. II. COSMOLOGICAL MODELS AND PRECISION EMULATION OF THE NONLINEAR MATTER POWER SPECTRUM

David Higdon, Katrin Heitmann, Brian J. Williams, 2009, 0902.0429.

Cosmic Calibration

David Higdon, Katrin Heitmann, Salman Habib, 2006 .

Modeling satellite drag coefficients with response surfaces

Earl Lawrence, David Higdon, Richard Linares, 2014 .

Nonequilibrium and nonlinear dynamics in Berea and Fontainebleau sandstones: Low‐strain regime

David Higdon, Paul A. Johnson, Katrin Heitmann, 2006, cond-mat/0601120.

Nonlinear and nonequilibrium dynamics in geomaterials.

David Higdon, Katrin Heitmann, Donatella Pasqualini, 2004, Physical review letters.

THE COYOTE UNIVERSE EXTENDED: PRECISION EMULATION OF THE MATTER POWER SPECTRUM

Earl Lawrence, David Higdon, Katrin Heitmann, 2013, 1304.7849.

Computational Enhancements to Bayesian Design of Experiments Using Gaussian Processes

Christine M. Anderson-Cook, Brian P. Weaver, David Higdon, 2016 .

On the validation of seismic imaging methods: Finite frequency or ray theory?

Monica Maceira, David Higdon, Richard M. Allen, 2015 .

Inference for a Proton Accelerator Using Convolution Models

David Higdon, Bruno Sansó, Herbert K. H. Lee, 2008 .

Multiscale Data Integration Using Markov Random Fields

Akhil Datta-Gupta, David Higdon, Sang Heon Lee, 2002 .

Dynamic linear models for forecasting of radiation belt electrons and limitations on physical interpretation of predictive models

Brian P. Weaver, David Higdon, Dave Osthus, 2014 .

The Mira-Titan Universe. II. Matter Power Spectrum Emulation

Hal Finkel, Earl Lawrence, David Higdon, 2017, 1705.03388.

Inferring particle distribution in a proton accelerator experiment

David Higdon, Bruno Sansó, Herbert K. H. Lee, 2006 .

Hierarchical Bayesian Analysis and the Preston-Tonks-Wallace Model

David Higdon, Kenneth M. Hanson, Cetin Unal, 2007 .

THE MIRA–TITAN UNIVERSE: PRECISION PREDICTIONS FOR DARK ENERGY SURVEYS

Hal Finkel, Earl Lawrence, David Higdon, 2015, 1508.02654.