Estimating the Discrepancy Between Computer Model Data and Field Data: Modeling Techniques for Deterministic and Stochastic Computer Simulators

Computer models have become useful research tools in many disciplines. In many cases a researcher has access to data from a computer simulator and from a physical system. This research discusses Bayesian models that allow for the estimation of the discrepancy between the two data sources. We fit two models to data in the field of electrical engineering. Using this data we illustrate ways of modeling both a deterministic and a stochastic simulator when specific parametric assumptions can be made about the discrepancy term.

Name thesis:degree_name

MS

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• Dastrup, Emily Joy

• Bayesian statistics
• computer models
• validation
• combining data
• Statistics and Probability

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/652

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-1651