Complexity arises in different fields of application. The increasing number of variables and system responses used to describe an experimental problem limits the applicability of classical approaches from Design of Experiments (DOE) and Sequential Experimental Design (SED). In this situation, more effort should be put into developing methodological approaches for complex multi-response experimental problems. In this work, we develop a novel design technique based on the incorporation of the Pareto optimality concept into the Bayesian sequential design framework. One of the crucial aspects of the approach involves the selection method of the next design points based on current information and the chosen system responses. The novel sequential approach has been tested on a simulated case study.

Borrotti, M., Pievatolo, A. (2016). A multi-objective Bayesian sequential design based on Pareto optimality. In mODa 11 - Advances in Model-Oriented Design and Analysis (pp.47-54) [10.1007/978-3-319-31266-8_6].

A multi-objective Bayesian sequential design based on Pareto optimality

Borrotti, M;Pievatolo, A
2016

Abstract

Complexity arises in different fields of application. The increasing number of variables and system responses used to describe an experimental problem limits the applicability of classical approaches from Design of Experiments (DOE) and Sequential Experimental Design (SED). In this situation, more effort should be put into developing methodological approaches for complex multi-response experimental problems. In this work, we develop a novel design technique based on the incorporation of the Pareto optimality concept into the Bayesian sequential design framework. One of the crucial aspects of the approach involves the selection method of the next design points based on current information and the chosen system responses. The novel sequential approach has been tested on a simulated case study.
paper
Bayesian approach; design of experiments; multi-objective problems
English
Workshop on Model-Oriented Data Analysis and Optimum Design (mODa11)
2016
mODa 11 - Advances in Model-Oriented Design and Analysis
978-3-319-31266-8
2016
47
54
none
Borrotti, M., Pievatolo, A. (2016). A multi-objective Bayesian sequential design based on Pareto optimality. In mODa 11 - Advances in Model-Oriented Design and Analysis (pp.47-54) [10.1007/978-3-319-31266-8_6].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/214764
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