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==Log-concave distributions==
Log-concave distributions are necessary for a number of algorithms, e.g. [[adaptive rejection sampling]]. Every distribution with log-concave density is a [[maximum entropy probability distribution]] with specified mean ''μ'' and [[Deviation risk measure]] ''D''.<ref name="Grechuk1">{{cite journal |last=Grechuk |first=B. |last2=Molyboha |first2=A. |last3=Zabarankin |first3=M. |year=2009 |title=Maximum Entropy Principle with General Deviation Measures |journal=Mathematics of Operations Research |volume=34 |issue=2 |pages=445–467 |doi=10.1287/moor.1090.0377 }}</ref>
As it happens, many common [[probability distribution]]s are log-concave. Some examples:<ref>See {{cite journal |first=Mark |last=Bagnoli |first2=Ted |last2=Bergstrom |year=2005 |title=Log-Concave Probability and Its Applications |journal=Economic Theory |volume=26 |issue=2 |pages=445–469 |doi=10.1007/s00199-004-0514-4 |url=http://www.econ.ucsb.edu/~tedb/Theory/delta.pdf }}</ref>
*The [[normal distribution]] and [[multivariate normal distribution]]s.
*The [[exponential distribution]].
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