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What is Bayesian Calibration of a Computer Model

Encyclopedia of Information Science and Technology, Third Edition
A method to choose effective model inputs that regards model inputs as unknown and unknowable parameters.
Published in Chapter:
Algorithms for Approximate Bayesian Computation
Tom Burr (Statistical Sciences, Los Alamos National Laboratory, USA) and Alexei Skurikhin (Space Data Systems, Los Alamos National Laboratory, USA)
DOI: 10.4018/978-1-4666-5888-2.ch149
Computer models have many applications in business, medicine, engineering, and science. A typical model has known inputs such as a task schedule, unknown inputs such as the average time to complete a task, and outputs such as product production quality and rates. Our focus is stochastic computer models, which output different results each time the model is run with the same inputs. Such models are calibrated by comparing real data to model predictions. A calibrated computer model can provide a cost-effective option to examine “what-if” questions such as “what if we could reduce the average time to complete a specific task.” This chapter describes an attractive option to calibrate a stochastic computer model that relies not on the raw data but on summary statistics derived from the real data.
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