Abstract
This chapter discusses mathematical methods for online automatic tuning. After formulating the abstract model of automatic tuning, we review the proposed method, which comprises several novel concepts of automatic tuning, such as online automatic tuning, Bayesian data analysis for quantitative treatments of uncertainties, Bayesian suboptimal sequential experimental design, asymptotic optimality, finite startup, and infinite dilution. Experimental results reveal that the Bayesian sequential experimental design has advantages over random sampling, although random sampling combined with an accurate cost function model can be as good as the Bayesian sequential experimental design.
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