Editing Principle of maximum entropy (section)

===Prior probabilities===
The principle of maximum entropy is often used to obtain [[prior probability|prior probability distributions]] for [[Bayesian inference]]. Jaynes was a strong advocate of this approach, claiming the maximum entropy distribution represented the least informative distribution.<ref>{{cite journal
 |last=Jaynes |first=E. T. |author-link = Edwin Thompson Jaynes
 |year=1968
 |url=http://bayes.wustl.edu/etj/articles/brandeis.pdf
 |title=Prior Probabilities
 |journal=IEEE Transactions on Systems Science and Cybernetics
 |volume=4 |issue=3 |pages=227–241
 |doi=10.1109/TSSC.1968.300117
}}</ref>
A large amount of literature is now dedicated to the elicitation of maximum entropy priors and links with [[channel coding]].<ref>{{cite journal
 |last=Clarke |first=B.
 |year=2006
 |title=Information optimality and Bayesian modelling
 |journal=[[Journal of Econometrics]]
 |volume=138 |issue=2 |pages=405–429
 |doi=10.1016/j.jeconom.2006.05.003 
}}</ref><ref>{{cite journal
 |doi=10.2307/2669786
 |last=Soofi |first=E.S. 
 |year=2000
 |title=Principal Information Theoretic Approaches
 |journal=[[Journal of the American Statistical Association]]
 |volume=95 |issue=452 |pages=1349–1353
 |mr=1825292 |jstor=2669786
}}</ref><ref>{{cite journal
 |last=Bousquet |first=N. 
 |year=2008
 |title=Eliciting vague but proper maximal entropy priors in Bayesian experiments
 |journal=Statistical Papers
 |volume=51
 |issue=3
 |doi=10.1007/s00362-008-0149-9 
 |pages=613–628
|s2cid=119657859 
 }}</ref><ref>{{Cite journal|title = Objective priors from maximum entropy in data classification|journal = Information Fusion|date = 2013-04-01|pages = 186–198|volume = 14|issue = 2|doi = 10.1016/j.inffus.2012.01.012|first1 = Francesco A. N.|last1 = Palmieri|first2 = Domenico|last2 = Ciuonzo|citeseerx = 10.1.1.387.4515}}</ref>