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Local and discrete scoring rules

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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.

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