{"data":{"id":"10.6084/m9.figshare.1423397","type":"dois","attributes":{"doi":"10.6084/m9.figshare.1423397","prefix":"10.6084","suffix":"m9.figshare.1423397","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"M. T. Pratola","affiliation":[],"nameIdentifiers":[]},{"name":"D. M. Higdon","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Bayesian Additive Regression Tree Calibration of Complex High-Dimensional Computer Models"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2015,"subjects":[{"subject":"Medicine"},{"subject":"Neuroscience"},{"subject":"59999 Environmental Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Earth and related environmental sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Earth and related environmental sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Sociology"},{"subject":"FOS: Sociology","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Sociology","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"69999 Biological Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"80699 Information Systems not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"19999 Mathematical Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mathematics","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Cancer"}],"contributors":[],"dates":[{"date":"2015-05-22","dateType":"Created"},{"date":"2016-05-20","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/00401706.2015.1049749","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["822879 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"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 CO\u003csub\u003e2\u003c/sub\u003e 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.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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