David M. Higdon


  • University of Washington, Statistics, Ph.D., 1994
  • University of California at San Diego, Mathematics, M.A., 1989
  • University of California at San Diego, Mathematics, B.A., 1987

David M. Higdon is a professor in the Social Decision Analytics Laboratory at the Biocomplexity Institute of Virginia Tech. Previously, he spent 10 years as a scientist or group leader of the Statistical Sciences Group at Los Alamos National Laboratory. He is an expert in Bayesian statistical modeling of environmental and physical systems, combining physical observations with computer simulation models for prediction and inference. Dr. Higdon has served on several advisory groups concerned with statistical modeling and uncertainty quantification and co-chaired the NRC Committee on Mathematical Foundations of Validation, Verification, and Uncertainty Quantification. He is a fellow of the American Statistical Association.

  • space-time modeling
  • inverse problems in hydrology and imaging
  • statistical modeling in ecology, environmental science, and biology
  • multiscale models
  • parallel processing in posterior exploration
  • statistical computing
  • Monte Carlo and simulation based methods


  • Higdon D, McDonnell JD, Schunck N, Sarich J, Wild SM. A Bayesian Approach for Parameter Estimation and Prediction using a Computationally Intensive Model. arXiv preprint arXiv:1407.3017. 2014. 
  • Osthus D, Caragea PC, Higdon D, Morley SK, Reeves GD, Weaver BP. Dynamic linear models for forecasting of radiation belt electrons and limitations on physical interpretation of predictive models. Space Weather. 2014;12:426–446. 
  • Pratola MT, Chipman HA, Gattiker JR, Higdon DM, McCulloch R, Rust WN. Parallel Bayesian Additive Regression Trees. Journal of Computational and Graphical Statistics. 2014;23:830–852.  
  • Storlie CB, Lane WA, Ryan EM, Gattiker JR, Higdon DM. Calibration of Computational Models with Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA. Journal of the American Statistical Association. 2014:00.  


  • Funsten HO, DeMajistre R, Frisch PC, et al. Circularity of the Interstellar Boundary Explorer ribbon of enhanced energetic neutral atom (ENA) flux. The Astrophysical Journal. 2013;776:30.  
  • Higdon D, Gattiker J, Lawrence E, et al. Computer Model Calibration Using the Ensemble Kalman Filter. Technometrics. 2013;55:488–500.  


  • Heitmann K, White M, Wagner C, Habib S, Higdon D. The coyote universe. I. Precision determination of the nonlinear matter power spectrum. The Astrophysical Journal. 2010;715:104.  
  • Holsclaw T, Alam U, Sansó B, et al. Nonparametric dark energy reconstruction from supernova data. Physical review letters. 2010;105:241302.  
  • Lawrence E, Heitmann K, White M, et al. The coyote universe. III. simulation suite and precision emulator for the nonlinear matter power spectrum. The Astrophysical Journal. 2010;713:1322.  


  • Heitmann K, Higdon D, White M, et al. The coyote universe. ii. cosmological models and precision emulation of the nonlinear matter power spectrum. The Astrophysical Journal. 2009;705:156.  
  • Vrugt JA, Ter Braak CJF, Diks CGH, Robinson BA, Hyman JM, Higdon D. Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. International Journal of Nonlinear Sciences and Numerical Simulation. 2009;10:273–290.  


  • Higdon D, Gattiker J, Williams B, Rightley M. Computer model calibration using high-dimensional output. Journal of the American Statistical Association. 2008;103.  


  • Habib S, Heitmann K, Higdon D, Nakhleh C, Williams B. Cosmic calibration: Constraints from the matter power spectrum and the cosmic microwave background. Physical Review D. 2007;76:083503.  


  • Heitmann K, Higdon D, Nakhleh C, Habib S. Cosmic calibration. The Astrophysical Journal Letters. 2006;646:L1.  
  • Linkletter C, Bingham D, Hengartner N, Higdon D, Kenny QY. Variable selection for Gaussian process models in computer experiments. Technometrics. 2006;48.  


  • Higdon D, Kennedy M, Cavendish JC, Cafeo JA, Ryne RD. Combining field data and computer simulations for calibration and prediction. SIAM Journal on Scientific Computing. 2004;26:448–466.  


  • Higdon D. Space and space-time modeling using process convolutions. In: Quantitative methods for current environmental issues. Springer London; 2002:37–56.   http://isbn.nu/1447111710
  • Higdon D, Lee H, Bi Z. A Bayesian approach to characterizing uncertainty in inverse problems using coarse and fine-scale information. Signal Processing, IEEE Transactions on. 2002;50:389–399.  


  • Besag J, Higdon D. Bayesian analysis of agricultural field experiments. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 1999;61:691–746.  
  • Higdon D, Swall J, Kern J. Non-stationary spatial modeling. Bayesian statistics. 1999;6:761–768.  


  • Higdon D. A process-convolution approach to modelling temperatures in the North Atlantic Ocean. Environmental and Ecological Statistics. 1998;5:173–190.  
  • Higdon DM. Auxiliary variable methods for Markov chain Monte Carlo with applications. Journal of the American Statistical Association. 1998;93:585–595.  


  • Besag J, Green P, Higdon D, Mengersen K. Bayesian computation and stochastic systems. Statistical science. 1995:3–41.