Editing Principle of maximum entropy (section)

===Probability density estimation ===
One of the main applications of the maximum entropy principle is in discrete and continuous [[density estimation]].<ref name="BK08">{{cite journal
 |last1=Botev |first1=Z. I. | last2=Kroese | first2=D. P.
 |year=2008
 |title=Non-asymptotic Bandwidth Selection for Density Estimation of Discrete Data
 |journal=Methodology and Computing in Applied Probability
 |volume=10 |issue=3 |pages=435
 |doi=10.1007/s11009-007-9057-z
|s2cid=122047337 }}</ref><ref name="BK11">{{cite journal
 |last1=Botev |first1=Z. I. | last2=Kroese | first2=D. P.
 |year=2011
 |title=The Generalized Cross Entropy Method, with Applications to Probability Density Estimation
 |journal=Methodology and Computing in Applied Probability
 |volume=13 |issue=1 |pages=1–27
 |doi=10.1007/s11009-009-9133-7
|s2cid=18155189 |url=http://espace.library.uq.edu.au/view/UQ:200564/UQ200564_preprint.pdf }}</ref>
Similar to [[support vector machine]] estimators, 
the maximum entropy principle may require the solution to a [[quadratic programming]] problem, and thus provide 
a sparse mixture model as the optimal density estimator. One important advantage of the method is its ability to incorporate prior information in the density estimation.<ref>{{cite book
 |last1=Kesavan|first1=H. K. | last2=Kapur  | first2=J. N.
 |year=1990
 |contribution=Maximum Entropy and Minimum Cross-Entropy Principles
 |title=Maximum Entropy and Bayesian Methods
 |url=https://archive.org/details/maximumentropyba00jayn_552|url-access=limited| editor-last= Fougère | editor-first= P. F. |pages=[https://archive.org/details/maximumentropyba00jayn_552/page/n418 419]–432
 |doi=10.1007/978-94-009-0683-9_29 |isbn=978-94-010-6792-8 }}</ref>