Lecture

Discriminant Analysis: Bayes Rule

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Description

This lecture covers the discriminant analysis, focusing on the Bayes discriminant rule. It explains how to allocate individuals to different populations based on measurements, considering prior probabilities. The lecture discusses the maximum likelihood discriminant rule, optimal properties in the case of equal costs, decision theory with unequal costs, and discrimination under estimation. It also presents the sample discriminant rule, likelihood ratio discriminant rule, and Fisher's Linear Discriminant Function.

Official source

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Statistical hypothesis testing

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