Calibration methods have been widely studied in survey sampling over the last
decades. Viewing calibration as an inverse problem, we extend the calibration
technique by using a maximum entropy method. Finding the optimal weights is
achieved by considering random weights and looking for a discrete distribution
which maximizes an entropy under the calibration constraint. This method points
a new frame for the computation of such estimates and the investigation of its
statistical properties.