Minimax Entropy and Maximum Likelihood. Complementarity of tasks, identity of solutions

Mathematics – Statistics Theory

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Presented at 'MaxEnt 2000', CNRS, Gif sur Yvette, France, July 8-13 2000

Scientific paper

10.1063/1.1381870

Concept of exponential family is generalized by simple and general exponential form. Simple and general potential are introduced. Maximum Entropy and Maximum Likelihood tasks are defined. ML task on the simple exponential form and ME task on the simple potentials are proved to be complementary in set-up and identical in solutions. ML task on the general exponential form and ME task on the general potentials are weakly complementary, leading to the same necessary conditions. A hypothesis about complementarity of ML and MiniMax Entropy tasks and identity of their solutions, brought up by a special case analytical as well as several numerical investigations, is suggested in this case. MiniMax Ent can be viewed as a generalization of MaxEnt for parametric linear inverse problems, and its complementarity with ML as yet another argument in favor of Shannon's entropy criterion.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Minimax Entropy and Maximum Likelihood. Complementarity of tasks, identity of solutions does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Minimax Entropy and Maximum Likelihood. Complementarity of tasks, identity of solutions, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Minimax Entropy and Maximum Likelihood. Complementarity of tasks, identity of solutions will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-630807

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.