Minimum cross-entropy estimation with inaccurate side information

Given a prior estimate of a probability, q, and a constraint /spl Sigma/p/sub i/x/sub i/=a, one well-known way of estimating p is to minimize the cross-entropy I(p; q) subject to the constraint. A modification to this method is proposed for use when the value a is only approximately known. The modification is based on the penalty function method in constrained optimization. It has an interpretation in differential geometry methods in statistics and it sometimes gives a maximum-likelihood estimate.