15 citations · 31 across the 9 of their papers we have counts for
9 papers
Sequential Monte Carlo for Cut-Bayesian Posterior Computation
Joseph Mathews, Giri Gopalan, James Gattiker +2
We propose a sequential Monte Carlo (SMC) method to efficiently and accurately compute cut-Bayesian posterior quantities of interest, variations of standard Bayesian approaches con…
Wildland Fire Mid-story: A generative modeling approach for representative fuels
Grant Hutchings, James Gattiker, Braden Scherting
Computational models for understanding and predicting fire in wildland and managed lands are increasing in impact. Data characterizing the fuels and environment is needed to contin…
ForestAlign: Automatic Forest Structure-based Alignment for Multi-view TLS and ALS Point Clouds
Juan Castorena, L. Turin Dickman, Adam J. Killebrew +3
Access to highly detailed models of heterogeneous forests, spanning from the near surface to above the tree canopy at varying scales, is increasingly in demand. This enables advanc…
A New Assessment Statement for the Trinity Nuclear Test, 75 Years Later
H. D. Selby, S. K. Hanson, D. Meininger +14
New measurement and assessment techniques have been applied to the radiochemical re-evaluation of the Trinity Event. Thirteen trinitite samples were dissolved and analyzed using a…
Applications of Quantum Annealing in Statistics
Robert C. Foster, Brian Weaver, James Gattiker
Quantum computation offers exciting new possibilities for statistics. This paper explores the use of the D-Wave machine, a specialized type of quantum computer, which performs quan…
Dynamic Bayesian Influenza Forecasting in the United States with Hierarchical Discrepancy
Dave Osthus, James Gattiker, Reid Priedhorsky +1
Timely and accurate forecasts of seasonal influenza would assist public health decision-makers in planning intervention strategies, efficiently allocating resources, and possibly s…
Calibration of Computational Models with Categorical Parameters and Correlated Outputs via Bayesian Smoothing Spline ANOVA
Curtis B. Storlie, William A. Lane, Emily M. Ryan +2
It has become commonplace to use complex computer models to predict outcomes in regions where data does not exist. Typically these models need to be calibrated and validated using…
Parallel Bayesian Additive Regression Trees
Matthew T. Pratola, Hugh A. Chipman, James R. Gattiker +3
Bayesian Additive Regression Trees (BART) is a Bayesian approach to flexible non-linear regression which has been shown to be competitive with the best modern predictive methods su…
Computer Model Calibration using the Ensemble Kalman Filter
Dave Higdon, Matt Pratola, James Gattiker +6
The ensemble Kalman filter (EnKF) (Evensen, 2009) has proven effective in quantifying uncertainty in a number of challenging dynamic, state estimation, or data assimilation, proble…