Editing Logit (section)

== Uses and properties ==
* The logit in [[logistic regression]] is a special case of a link function in a [[generalized linear model]]: it is the canonical [[link function]] for the [[Bernoulli distribution]].
* More abstractly, the logit is the [[natural parameter]] for the [[binomial distribution]]; see {{slink|Exponential family|Binomial distribution}}.
* The logit function is the negative of the [[derivative]] of the [[binary entropy function]].
* The logit is also central to the probabilistic [[Rasch model]] for [[measurement]], which has applications in psychological and educational assessment, among other areas.
* The inverse-logit function (i.e., the [[logistic function]]) is also sometimes referred to as the ''expit'' function.<ref>{{cite web |url=http://www.stat.ucl.ac.be/ISdidactique/Rhelp/library/msm/html/expit.html |title=R: Inverse logit function |access-date=2011-02-18 |url-status=dead |archive-url=https://web.archive.org/web/20110706132209/http://www.stat.ucl.ac.be/ISdidactique/Rhelp/library/msm/html/expit.html |archive-date=2011-07-06 }}</ref>
* In plant disease epidemiology, the logistic, Gompertz, and monomolecular models are collectively known as the Richards family models.
* The log-odds function of probabilities is often used in state estimation algorithms<ref>{{cite journal |last=Thrun|first=Sebastian |title=Learning Occupancy Grid Maps with Forward Sensor Models |journal=Autonomous Robots |language=en |volume=15|issue=2|pages=111–127 |doi=10.1023/A:1025584807625|issn=0929-5593|year=2003|s2cid=2279013 |url=https://mediawiki.isr.tecnico.ulisboa.pt/images/5/5b/Thrun03.pdf }}</ref> because of its numerical advantages in the case of small probabilities. Instead of multiplying very small floating point numbers, log-odds probabilities can just be summed up to calculate the (log-odds) joint probability.<ref>{{cite web |url=https://www.cs.cmu.edu/~16831-f12/notes/F12/16831_lecture05_vh.pdf |title=Statistical Techniques in Robotics |last=Styler|first=Alex |date=2012 |page=2 |access-date=2017-01-26 }}</ref><ref>{{cite journal |last1=Dickmann|first1=J. |last2=Appenrodt|first2=N. |last3=Klappstein|first3=J. |last4=Bloecher|first4=H. L. |last5=Muntzinger|first5=M. |last6=Sailer|first6=A. |last7=Hahn|first7=M. |last8=Brenk|first8=C. |date=2015-01-01 |title=Making Bertha See Even More: Radar Contribution |journal=IEEE Access |volume=3|pages=1233–1247 |doi=10.1109/ACCESS.2015.2454533|doi-access=free |issn=2169-3536|bibcode=2015IEEEA...3.1233D }}</ref>