Abstract
In this paper we propose a new technique of generating optimal designs by means of simulation. The method combines ideas from approximate Bayesian computation and optimal design of experiments and allows great flexibility in the employed criteria and models. We illustrate the idea by a simple expository example.
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Acknowledgements
We thank Helga Wagner for fruitful discussions on MCMC methods and the FWF/ANR project DESIRE I-833-N18 for partial support. We thank our reviewers for their valuable suggestions.
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Hainy, M., Müller, W.G., Wynn, H.P. (2013). Approximate Bayesian Computation Design (ABCD), an Introduction. In: Ucinski, D., Atkinson, A., Patan, M. (eds) mODa 10 – Advances in Model-Oriented Design and Analysis. Contributions to Statistics. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00218-7_16
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DOI: https://doi.org/10.1007/978-3-319-00218-7_16
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00217-0
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