Proper scoring rule: Difference between revisions

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A proper scoring rule is propera ifsubset theof expectedscoring scorerules iswhich maximized whenincentivize the forecaster reportsto report their true subjective probability distribution.
 
For the binary case, suppose a forecaster believes the outcome will occur with probability <math>p</math> and reports belief <math>b</math>, which will be scored by the rule <math>f</math>. We call <math>f</math> proper if <math>E[f(b, x)]</math> is maximized when <math>b = p</math>. We call <math>f</math> strictly proper if <math>f</math> is proper and <math>E[f(b, x)]</math> is maximized only for <math>b = p</math>.
 
== Examples ==
 
{{Wikipedia|Scoring_rule#StrictlyProperScoringRules}}
* [[Log score]]
 
* [[Brier score]]
* More on [https://en.wikipedia.org/wiki/Scoring_rule#Examples_of_strictly_proper_scoring_rules Wikipedia]
 
[[Category:Proper scoring rules]]