  In this paper we pose the question: After gathering N data points, at what
value of the control parameter should the next measurement be done?
  We propose an on-line algorithm which samples optimally by maximizing the
gain in information on the parameters to be measured. We show analytically that
the information gain is maximum for those potential measurements whose outcome
is most unpredictable, i.e. for which the predictive distribution has maximum
entropy. The resulting algorithm is applied to exponential analysis.
