For instance, if one happens to choose the maximum value of the control parameter to be much larger than the unknown decay constant T many samples would obviously be "pure noise", thus reducing the accuracy of the estimate of T.
Such difficulties in parameter estimation can be avoided by posing the question: Given the information of N data points, at which value of the control parameters should the next measurement be done? Answered in a Bayesian setting this question will lead to a criterion for the design of such experiments based on a minimax principle: The maximum gain in the information about the parameters is obtained by looking at the point where the predictive certainty is the least. In other words, the entropy of the posterior distribution of parameter values is minimized by taking measurements for those values of the control parameters where the entropy of the predictive distribution is maximum.
Based on this minimax result, an on-line algorithm will be proposed which samples optimally in the sense of maximum information gain per measurement. The algorithm will be applied to a problem of multiexponential analysis.
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