M.D. I. Estimation via Unconstrained Convex Programming.

  title={M.D. I. Estimation via Unconstrained Convex Programming.},
  author={Patrick L. Brockett and Abraham Charnes and William Cooper},
A method is presented for obtaining minimum discrimination information (M.D.I.) estimates of probability distributions. This involves using an extremal principle of Charnes and Cooper (1974) and, viewing M.D.I, estimation in a dual convex programming framework. The resulting dual convex program is unconstrained and involves only exponential and linear terms, and hence is easily