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Algorithms for Approximate Bayesian Computation

Algorithms for Approximate Bayesian Computation
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Author(s): Tom Burr (Statistical Sciences, Los Alamos National Laboratory, USA)and Alexei Skurikhin (Space Data Systems, Los Alamos National Laboratory, USA)
Copyright: 2015
Pages: 9
Source title: Encyclopedia of Information Science and Technology, Third Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-4666-5888-2.ch149


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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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