The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
Open accessAbstract
Motivated by a computer experiment for the design of a rocket booster, this article explores nonstationary modeling methodologies that couple stationary Gaussian processes with treed partitioning. Partitioning is a simple but effective method for dealing with nonstationarity. The methodological developments and statistical computing details that make this approach efficient are described in detail. In addition to providing an analysis of the rocket booster simulator, we show that our approach is effective in other arenas as well.
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Citations
Cites
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Bayesian calibration of computer models
Kennedy, M. C., & O'Hagan, A. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00294 · Cited by 4,222
Some aspects of the spline smoothing approach to non-parametric regression curve fitting
Silverman, B. W. (1985) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1985.tb01327.x · Cited by 1,117
Cited by
A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions
Schulz, E., Speekenbrink, M., & Krause, A. (2018) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2018.03.001 · Cited by 1,735
Choosing the Sample Size of a Computer Experiment: A Practical Guide
Loeppky, J. L., Sacks, J., & Welch, W. J. (2009) · Technometrics · doi:10.1198/tech.2009.08040 · Cited by 758
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets
Datta, A., Banerjee, S., Finley, A. O., & Gelfand, A. E. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2015.1044091 · Cited by 615
Dynamic Trees for Learning and Design
Taddy, M. A., Gramacy, R. B., & Polson, N. G. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.ap09769 · Cited by 113
Self-citationBayesian joint analysis of heterogeneous genomics data
Ray, P., Zheng, L., Lucas, J., & Carin, L. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu064 · Cited by 84
Metadata
- Authors
- Gramacy, R. B., & Lee, H. K. H.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2008-09
- Volume
- 103
- Issue
- 483
- Pages
- 1119-1130
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2092271904
- Cited by
- 650
- References
- 3
- FWCI
- 39.75
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
