Year |
Venue |
Citation |
Link |
2024 |
arXiv |
Hutchings, G., Rumsey, K., Bingham, D., & Huerta, G. (2024). Enhancing Approximate Modular Bayesian Inference by Emulating the Conditional Posterior. arXiv. |
link |
2024 |
Technometrics |
Rumsey, K., Hardy, Z.K., Ahrens, C., & Vander Wiel, S. (2024). Co-Active Subspace Methods for the Joint Analysis of Adjacent Computer Models. Technometrics. |
link |
2024 |
SIAM/ASA JUQ |
Rumsey, K. N., Francom, D., & Shen, A. (2024). Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation. SIAM/ASA Journal on Uncertainty Quantification, 12(2), 646-666. |
link |
2023 |
JCGS |
Rumsey, K., Francom, D., & Vander Wiel, S. (2024). Discovering Active Subspaces for High-Dimensional Computer Models. Journal of Computational and Graphical Statistics, 1-13. |
link |
2023 |
Stat & Computing |
Collins, G., Francom, D., & Rumsey, K. (2024). Bayesian Projection Pursuit Regression Statistics and Computing, 34(1), 29. |
link |
2023 |
Stat |
Rumsey, K., Huerta, G. & Tucker, J.D. (2023, April). A localized ensemble of approximate Gaussian processes for fast emulation in sequential settings. Stat, e576. |
link |
2022 |
Applications of Machine Learning 2022 |
Rumsey, K., Grosskopf, M., Lawrence, E., Biswas, A., & Urban, N. (2022, October). A hierarchical sparse Gaussian process for in situ inference in expensive physics simulations. In Applications of Machine Learning 2022 (Vol. 12227, pp. 126-138). SPIE. |
link |
2021 |
ISAV'21 |
Grosskopf, M., Lawrence, E., Biswas, A., Tang, L., Rumsey, K., Van Roekel, L., & Urban, N. (2021). In-situ spatial inference on climate simulations with sparse gaussian processes. In ISAV'21: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (pp. 31-36). |
link |
2021 |
JSCS |
Rumsey, K. N., & Huerta, G. (2021). Fast matrix algebra for Bayesian model calibration. Journal of Statistical Computation and Simulation, 91(7), 1331-1341. |
link |
2020 |
SIAM/ASA JUQ |
Rumsey, K., Huerta, G., Brown, J., & Hund, L. (2020). Dealing with measurement uncertainties as nuisance parameters in Bayesian model calibration. SIAM/ASA Journal on Uncertainty Quantification, 8(4), 1287-1309. |
link |
2018 |
Reliability Engineering and System Safety |
Hund, L., Schroeder, B., Rumsey, K., & Huerta, G. (2018). Distinguishing between model-and data-driven inferences for high reliability statistical predictions. Reliability Engineering & System Safety, 180, 201-210. |
link |
2018 |
SAND-Report |
Hund, L., Schroeder, B. B., Rumsey, K., & Murchison, N. (2017). Robust approaches to quantification of margin and uncertainty for sparse data (No. SAND2017-9960). Sandia National Lab.(SNL-NM), Albuquerque, NM (United States). |
link |