University of Cambridge > > Statistics > Local and discrete scoring rules

Local and discrete scoring rules

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact R.B.Gramacy.

At its simplest, a scoring rule is a loss function for choosing a certain distribution to represent the uncertainty of a random variable. (A scoring rule is not a score function!) One of the key requirements is honesty: the expected score must be minmised by choosing the true distribution—- if it is known. I take an introductory tour of the role of scoring rules in decision theory and statistical inference and of their connection to entropy and geometry. I end with a discussion of recent advances in the use of scoring rules in cases of both continuous and discrete sample spaces.

This talk is part of the Statistics series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2024, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity