Name
Title | ||
|---|---|---|
Bayesian Additive Regression Tree Calibration of Complex High-Dimensional Computer Models. |
Abstract | ||
|---|---|---|
Complex natural phenomena are increasingly investigated by the use of a complex computer simulator. To leverage the advantages of simulators, observational data need to be incorporated in a probabilistic framework so that uncertainties can be quantified. A popular framework for such experiments is the statistical computer model calibration experiment. A limitation often encountered in current statistical approaches for such experiments is the difficulty in modeling high-dimensional observational datasets and simulator outputs as well as high-dimensional inputs. As the complexity of simulators seems to only grow, this challenge will continue unabated. In this article, we develop a Bayesian statistical calibration approach that is ideally suited for such challenging calibration problems. Our approach leverages recent ideas from Bayesian additive regression Tree models to construct a random basis representation of the simulator outputs and observational data. The approach can flexibly handle high-dimensional datasets, high-dimensional simulator inputs, and calibration parameters while quantifying important sources of uncertainty in the resulting inference. We demonstrate our methodology on a CO2 emissions rate calibration problem, and on a complex simulator of subterranean radionuclide dispersion, which simulates the spatial-temporal diffusion of radionuclides released during nuclear bomb tests at the Nevada Test Site. Supplementary computer code and datasets are available online. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1080/00401706.2015.1049749 | TECHNOMETRICS |
Keywords | Field | DocType |
Catastrophe model,Climate change,Markov chain Monte Carlo,Nonparametric,Treaty verification,Uncertainty quantification | Econometrics,Catastrophe modeling,Decision tree,Observational study,Uncertainty quantification,Markov chain Monte Carlo,Nonparametric statistics,Statistics,Calibration,Mathematics,Bayesian probability | Journal |
Volume | Issue | ISSN |
58.0 | 2.0 | 0040-1706 |
Citations | PageRank | References |
2 | 0.47 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew T. Pratola | 1 | 4 | 0.85 |
| David Higdon | 2 | 61 | 14.71 |