Abstract
The traditional numerical analysis considers optimization algorithms which guarantee some accuracy for all functions to be optimized. This includes the exact algorithms (that is the worst case analysis). Limiting the maximal error requires a computational effort that in many cases increases exponentially with the size of the problem. The alternative is average case analysis where the average error is made as small as possible. The average is taken over a set of functions to be optimized. The average case analysis is called the Bayesian Approach (BA) [4, 12].
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